{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Logs/Appendix_SurveyEvidence_Tables.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}23 Oct 2025, 12:50:34

{com}. do "/var/folders/54/7cxl8ny95nb4vs26j1q2yq9r0000gn/T//SD08037.000000"
{txt}
{com}. 
. /*-----------------------------------------------------------------------------*
> * REPLICATION for "Do Provinces that Win the Spanish Christmas Lottery 
> Experience a Surge in  Incumbent Popularity? An Out-of-Sample Replication of 
> Bagues and Esteve-Volart (2016)". JPE Micro
> * Authors: Carolina Bernal, Donald Green, and David Vilalta
> 
> * What for: Code for Individual Support for the Incumbent Party from Survey 
> Data -- Appendix Tables A19 to A29
> * Do file's author: Carolina Bernal
> **----------------------------------------------------------------------------*/
. 
. cls
{txt}
{com}. 
. global folder "/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG"
{txt}
{com}. 
. cd "${c -(}folder{c )-}"
{res}/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG
{txt}
{com}. 
. 
. * Path to figures and tables folders: 
. global tables "Results/Tables_Survey/"
{txt}
{com}. global figures "Results/Figures_Survey/" 
{txt}
{com}. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLES 19: Effects of Last Year's Lottery Prizes on Survey Measures 
. *** of Incumbent Party Support, by Data Source, Sample Period, and Survey Date
. **----------------------------------------------------------------------------**
. 
. 
. use "Data/B&EV_repData/database surveys voting final.dta", clear
{txt}
{com}. 
. 
. drop if province==""
{txt}(1,350 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. preserve
{txt}
{com}. 
. import delimited "Data/Our_data/year_month_survey.csv", clear
{res}{txt}(encoding automatically selected: ISO-8859-9)
{res}{text}(3 vars, 408 obs)

{com}. 
. 
. tempfile year_survey
{txt}
{com}. 
. save `year_survey'
{txt}{p 0 4 2}
file {bf}
/var/folders/54/7cxl8ny95nb4vs26j1q2yq9r0000gn/T//S_08037.000002{rm}
saved
as .dta format
{p_end}

{com}. 
. restore
{txt}
{com}. 
. ** Merge in mapping of survey number and survey year/month
. merge m:1 survey using `year_survey'
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             314
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             314{txt}  (_merge==2)

{col 5}Matched{col 30}{res}         295,247{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge!=3 
{txt}(314 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. gen yearmonth=ym(year, month)
{txt}
{com}. format yearmonth %tm
{txt}
{com}. label var yearmonth "Year-Month"
{txt}
{com}. 
. encode fixed, gen(fixed_num)
{txt}
{com}. 
. global individual_characteristics "i.municipality_size female age i.education i.status"
{txt}
{com}. 
. rename (top_prizes_gdp expenditure_gdp)(top_prizes_gdp_ours expenditure_gdp_ours) // I'm changing the variables' name temporarily, just to match the names in the table.
{res}{txt}
{com}. 
. ** Col 1, Appendix Table 19: 
. eststo a_reppkg: areg vote_incumbent top_prizes_gdp_ours expenditure_gdp_ours i.survey $individual_characteristics, absorb(fixed_num) cluster(province)
{res}{txt}{p 0 6 2}note: {bf:2508.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2316.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2444.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2274.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2110.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2367.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2002.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2589.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2454.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:1840.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2728.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:1913.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2616.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:1979.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2672.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2406.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2769.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2225.survey} omitted because of collinearity with the absorbed variable.{p_end}
{txt}{p 0 6 2}note: {bf:2815.survey} omitted because of collinearity with the absorbed variable.{p_end}

{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:282,798}
{txt}{col 1}Absorbed variable: {res:fixed_num}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:1,190}
{txt}{col 52}{lalign 17:{help j_robustsingular##|_new:F(50, 49)}}{col 69} = {res}{ralign 7:.}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:.}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0522}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0479}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4375}

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:province})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 37}2651  {c |}{col 44}{res}{space 2} .0298244{col 56}{space 2} .0123615{col 67}{space 1}    2.41{col 76}{space 3}0.020{col 84}{space 4}  .004983{col 97}{space 3} .0546658
{txt}{space 37}2657  {c |}{col 44}{res}{space 2} .0255208{col 56}{space 2} .0133499{col 67}{space 1}    1.91{col 76}{space 3}0.062{col 84}{space 4}-.0013068{col 97}{space 3} .0523485
{txt}{space 37}2672  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2700  {c |}{col 44}{res}{space 2} -.022333{col 56}{space 2} .0140624{col 67}{space 1}   -1.59{col 76}{space 3}0.119{col 84}{space 4}-.0505924{col 97}{space 3} .0059265
{txt}{space 37}2728  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2735  {c |}{col 44}{res}{space 2}-.0028416{col 56}{space 2} .0151493{col 67}{space 1}   -0.19{col 76}{space 3}0.852{col 84}{space 4}-.0332852{col 97}{space 3}  .027602
{txt}{space 37}2750  {c |}{col 44}{res}{space 2}-.0199575{col 56}{space 2} .0133857{col 67}{space 1}   -1.49{col 76}{space 3}0.142{col 84}{space 4}-.0468572{col 97}{space 3} .0069421
{txt}{space 37}2761  {c |}{col 44}{res}{space 2} .0753512{col 56}{space 2}  .012014{col 67}{space 1}    6.27{col 76}{space 3}0.000{col 84}{space 4} .0512081{col 97}{space 3} .0994943
{txt}{space 37}2769  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2775  {c |}{col 44}{res}{space 2} .0166481{col 56}{space 2} .0146172{col 67}{space 1}    1.14{col 76}{space 3}0.260{col 84}{space 4}-.0127262{col 97}{space 3} .0460223
{txt}{space 37}2782  {c |}{col 44}{res}{space 2}-.0071712{col 56}{space 2} .0134659{col 67}{space 1}   -0.53{col 76}{space 3}0.597{col 84}{space 4} -.034232{col 97}{space 3} .0198895
{txt}{space 37}2798  {c |}{col 44}{res}{space 2} .0694618{col 56}{space 2} .0116328{col 67}{space 1}    5.97{col 76}{space 3}0.000{col 84}{space 4} .0460848{col 97}{space 3} .0928389
{txt}{space 37}2811  {c |}{col 44}{res}{space 2} .0469361{col 56}{space 2} .0135191{col 67}{space 1}    3.47{col 76}{space 3}0.001{col 84}{space 4} .0197685{col 97}{space 3} .0741038
{txt}{space 37}2815  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0048535{col 56}{space 2} .0063202{col 67}{space 1}   -0.77{col 76}{space 3}0.446{col 84}{space 4}-.0175545{col 97}{space 3} .0078474
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0073202{col 56}{space 2} .0078126{col 67}{space 1}   -0.94{col 76}{space 3}0.353{col 84}{space 4}-.0230201{col 97}{space 3} .0083797
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0128631{col 56}{space 2}  .008558{col 67}{space 1}   -1.50{col 76}{space 3}0.139{col 84}{space 4} -.030061{col 97}{space 3} .0043348
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0009028{col 56}{space 2} .0084364{col 67}{space 1}   -0.11{col 76}{space 3}0.915{col 84}{space 4}-.0178564{col 97}{space 3} .0160507
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0260649{col 56}{space 2} .0142587{col 67}{space 1}   -1.83{col 76}{space 3}0.074{col 84}{space 4}-.0547188{col 97}{space 3}  .002589
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0144964{col 56}{space 2} .0068158{col 67}{space 1}   -2.13{col 76}{space 3}0.038{col 84}{space 4}-.0281933{col 97}{space 3}-.0007994
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0174625{col 56}{space 2} .0023585{col 67}{space 1}   -7.40{col 76}{space 3}0.000{col 84}{space 4} -.022202{col 97}{space 3}-.0127229
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0005881{col 56}{space 2} .0001786{col 67}{space 1}    3.29{col 76}{space 3}0.002{col 84}{space 4} .0002292{col 97}{space 3} .0009469
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0203565{col 56}{space 2} .0058987{col 67}{space 1}   -3.45{col 76}{space 3}0.001{col 84}{space 4}-.0322103{col 97}{space 3}-.0085027
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0258975{col 56}{space 2} .0103369{col 67}{space 1}   -2.51{col 76}{space 3}0.016{col 84}{space 4}-.0466704{col 97}{space 3}-.0051247
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0090355{col 56}{space 2} .0047923{col 67}{space 1}   -1.89{col 76}{space 3}0.065{col 84}{space 4} -.018666{col 97}{space 3}  .000595
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0288111{col 56}{space 2} .0043579{col 67}{space 1}    6.61{col 76}{space 3}0.000{col 84}{space 4} .0200535{col 97}{space 3} .0375686
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0272944{col 56}{space 2} .0040124{col 67}{space 1}   -6.80{col 76}{space 3}0.000{col 84}{space 4}-.0353577{col 97}{space 3}-.0192311
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0147918{col 56}{space 2} .0032222{col 67}{space 1}    4.59{col 76}{space 3}0.000{col 84}{space 4} .0083165{col 97}{space 3} .0212672
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3131996{col 56}{space 2} .0145586{col 67}{space 1}   21.51{col 76}{space 3}0.000{col 84}{space 4} .2839431{col 97}{space 3} .3424561
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. estadd local SurveyFE "$\checkmark$"

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

{com}. estadd local ProvinceFE "$\checkmark$"

{txt}added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

{com}. estadd local YearFE "$\times$"

{txt}added macro:
             e(YearFE) : "{res:$\times$}"

{com}. estadd local PeriodFE "$\times$"

{txt}added macro:
           e(PeriodFE) : "{res:$\times$}"

{com}. estadd local PeriodCtrols "$\times$"

{txt}added macro:
       e(PeriodCtrols) : "{res:$\times$}"

{com}. estadd local Data "Repl.Pkg"

{txt}added macro:
               e(Data) : "{res:Repl.Pkg}"

{com}. estadd local Sample "Pre-10"

{txt}added macro:
             e(Sample) : "{res:Pre-10}"

{com}. estadd local Propensity "$\times$"

{txt}added macro:
         e(Propensity) : "{res:$\times$}"

{com}. estadd local Estimation "OLS"

{txt}added macro:
         e(Estimation) : "{res:OLS}"

{com}. estadd local Outcome "All year"

{txt}added macro:
            e(Outcome) : "{res:All year}"

{com}. 
. ** Note that this is province*year FE
. 
. * Run boottest to get the bootstrap confidence sets
. boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    2.0547
{col 25}{txt}Prob>|t| = {res}    0.0781

95%{txt} confidence set for null hypothesis expression: {res}[−.003163, .03022]
{txt}
{com}. 
. gen sample_pkg = 1 if e(sample)==1
{txt}(12,449 missing values generated)

{com}. 
. ** Col 2, Appendix Table 19:  
. eststo a_reppkg_Q1: areg vote_incumbent top_prizes_gdp_ours expenditure_gdp_ours i.survey $individual_characteristics if month<4, absorb(prov_num) cluster(province)
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:113,951}
{txt}{col 1}Absorbed variable: {res:prov_num}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:50}
{txt}{col 52}{lalign 17:F({res:38}, {res:49})}{col 69} = {res}{ralign 7:90.31}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0357}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0350}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4427}

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:province})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0109801{col 56}{space 2} .0127135{col 67}{space 1}    0.86{col 76}{space 3}0.392{col 84}{space 4}-.0145686{col 97}{space 3} .0365287
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0003737{col 56}{space 2} .0571612{col 67}{space 1}    0.01{col 76}{space 3}0.995{col 84}{space 4} -.114496{col 97}{space 3} .1152434
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1725  {c |}{col 44}{res}{space 2} .0742096{col 56}{space 2} .0188371{col 67}{space 1}    3.94{col 76}{space 3}0.000{col 84}{space 4} .0363551{col 97}{space 3} .1120642
{txt}{space 37}1785  {c |}{col 44}{res}{space 2} .0016838{col 56}{space 2} .0200716{col 67}{space 1}    0.08{col 76}{space 3}0.933{col 84}{space 4}-.0386515{col 97}{space 3} .0420192
{txt}{space 37}1858  {c |}{col 44}{res}{space 2} .0723653{col 56}{space 2} .0195243{col 67}{space 1}    3.71{col 76}{space 3}0.001{col 84}{space 4} .0331297{col 97}{space 3} .1116008
{txt}{space 37}1913  {c |}{col 44}{res}{space 2} .0179303{col 56}{space 2} .0193976{col 67}{space 1}    0.92{col 76}{space 3}0.360{col 84}{space 4}-.0210508{col 97}{space 3} .0569113
{txt}{space 37}1989  {c |}{col 44}{res}{space 2}  .006253{col 56}{space 2} .0158507{col 67}{space 1}    0.39{col 76}{space 3}0.695{col 84}{space 4}-.0256003{col 97}{space 3} .0381062
{txt}{space 37}2045  {c |}{col 44}{res}{space 2}-.0484163{col 56}{space 2} .0160696{col 67}{space 1}   -3.01{col 76}{space 3}0.004{col 84}{space 4}-.0807094{col 97}{space 3}-.0161232
{txt}{space 37}2077  {c |}{col 44}{res}{space 2}-.0392005{col 56}{space 2} .0161373{col 67}{space 1}   -2.43{col 76}{space 3}0.019{col 84}{space 4}-.0716297{col 97}{space 3}-.0067713
{txt}{space 37}2130  {c |}{col 44}{res}{space 2}-.0249339{col 56}{space 2} .0146943{col 67}{space 1}   -1.70{col 76}{space 3}0.096{col 84}{space 4}-.0544632{col 97}{space 3} .0045955
{txt}{space 37}2204  {c |}{col 44}{res}{space 2}-.0155799{col 56}{space 2} .0179022{col 67}{space 1}   -0.87{col 76}{space 3}0.388{col 84}{space 4}-.0515557{col 97}{space 3}  .020396
{txt}{space 37}2233  {c |}{col 44}{res}{space 2} -.033257{col 56}{space 2}  .016796{col 67}{space 1}   -1.98{col 76}{space 3}0.053{col 84}{space 4}-.0670099{col 97}{space 3}  .000496
{txt}{space 37}2274  {c |}{col 44}{res}{space 2} -.015508{col 56}{space 2} .0167568{col 67}{space 1}   -0.93{col 76}{space 3}0.359{col 84}{space 4}-.0491821{col 97}{space 3} .0181661
{txt}{space 37}2316  {c |}{col 44}{res}{space 2} .0083918{col 56}{space 2} .0213787{col 67}{space 1}    0.39{col 76}{space 3}0.696{col 84}{space 4}-.0345704{col 97}{space 3}  .051354
{txt}{space 37}2382  {c |}{col 44}{res}{space 2}  .041359{col 56}{space 2} .0169426{col 67}{space 1}    2.44{col 76}{space 3}0.018{col 84}{space 4} .0073116{col 97}{space 3} .0754063
{txt}{space 37}2406  {c |}{col 44}{res}{space 2} .0117933{col 56}{space 2} .0237092{col 67}{space 1}    0.50{col 76}{space 3}0.621{col 84}{space 4}-.0358522{col 97}{space 3} .0594388
{txt}{space 37}2444  {c |}{col 44}{res}{space 2} .0325809{col 56}{space 2} .0160572{col 67}{space 1}    2.03{col 76}{space 3}0.048{col 84}{space 4} .0003128{col 97}{space 3} .0648491
{txt}{space 37}2477  {c |}{col 44}{res}{space 2} -.028582{col 56}{space 2} .0164153{col 67}{space 1}   -1.74{col 76}{space 3}0.088{col 84}{space 4}-.0615697{col 97}{space 3} .0044057
{txt}{space 37}2555  {c |}{col 44}{res}{space 2} .0082307{col 56}{space 2} .0175674{col 67}{space 1}    0.47{col 76}{space 3}0.641{col 84}{space 4}-.0270724{col 97}{space 3} .0435338
{txt}{space 37}2589  {c |}{col 44}{res}{space 2} .1071057{col 56}{space 2} .0201938{col 67}{space 1}    5.30{col 76}{space 3}0.000{col 84}{space 4} .0665247{col 97}{space 3} .1476866
{txt}{space 37}2633  {c |}{col 44}{res}{space 2} .0362529{col 56}{space 2} .0231467{col 67}{space 1}    1.57{col 76}{space 3}0.124{col 84}{space 4}-.0102622{col 97}{space 3} .0827679
{txt}{space 37}2672  {c |}{col 44}{res}{space 2} .0317458{col 56}{space 2} .0183407{col 67}{space 1}    1.73{col 76}{space 3}0.090{col 84}{space 4}-.0051113{col 97}{space 3} .0686028
{txt}{space 37}2750  {c |}{col 44}{res}{space 2} .0440404{col 56}{space 2} .0123058{col 67}{space 1}    3.58{col 76}{space 3}0.001{col 84}{space 4} .0193109{col 97}{space 3} .0687698
{txt}{space 37}2782  {c |}{col 44}{res}{space 2}  .033585{col 56}{space 2} .0201565{col 67}{space 1}    1.67{col 76}{space 3}0.102{col 84}{space 4}-.0069211{col 97}{space 3}  .074091
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0082624{col 56}{space 2} .0082999{col 67}{space 1}   -1.00{col 76}{space 3}0.324{col 84}{space 4}-.0249416{col 97}{space 3} .0084167
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} -.017318{col 56}{space 2} .0096148{col 67}{space 1}   -1.80{col 76}{space 3}0.078{col 84}{space 4}-.0366397{col 97}{space 3} .0020038
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0378821{col 56}{space 2} .0114374{col 67}{space 1}   -3.31{col 76}{space 3}0.002{col 84}{space 4}-.0608663{col 97}{space 3}-.0148978
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} -.009685{col 56}{space 2} .0102714{col 67}{space 1}   -0.94{col 76}{space 3}0.350{col 84}{space 4}-.0303262{col 97}{space 3} .0109561
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0345049{col 56}{space 2} .0164792{col 67}{space 1}   -2.09{col 76}{space 3}0.041{col 84}{space 4}-.0676211{col 97}{space 3}-.0013887
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0286054{col 56}{space 2}   .01026{col 67}{space 1}   -2.79{col 76}{space 3}0.008{col 84}{space 4}-.0492237{col 97}{space 3} -.007987
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0173555{col 56}{space 2} .0033769{col 67}{space 1}   -5.14{col 76}{space 3}0.000{col 84}{space 4}-.0241417{col 97}{space 3}-.0105693
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0011141{col 56}{space 2} .0001912{col 67}{space 1}    5.83{col 76}{space 3}0.000{col 84}{space 4} .0007299{col 97}{space 3} .0014983
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0006665{col 56}{space 2} .0068592{col 67}{space 1}   -0.10{col 76}{space 3}0.923{col 84}{space 4}-.0144505{col 97}{space 3} .0131175
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0016859{col 56}{space 2} .0101806{col 67}{space 1}    0.17{col 76}{space 3}0.869{col 84}{space 4}-.0187728{col 97}{space 3} .0221446
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0194724{col 56}{space 2} .0071264{col 67}{space 1}   -2.73{col 76}{space 3}0.009{col 84}{space 4}-.0337935{col 97}{space 3}-.0051513
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}  .013705{col 56}{space 2} .0062089{col 67}{space 1}    2.21{col 76}{space 3}0.032{col 84}{space 4} .0012277{col 97}{space 3} .0261822
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} -.022883{col 56}{space 2} .0064221{col 67}{space 1}   -3.56{col 76}{space 3}0.001{col 84}{space 4}-.0357887{col 97}{space 3}-.0099773
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0081276{col 56}{space 2} .0044029{col 67}{space 1}    1.85{col 76}{space 3}0.071{col 84}{space 4}-.0007204{col 97}{space 3} .0169756
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2327598{col 56}{space 2}   .02486{col 67}{space 1}    9.36{col 76}{space 3}0.000{col 84}{space 4} .1828019{col 97}{space 3} .2827178
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. estadd local SurveyFE "$\checkmark$"

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

{com}. estadd local ProvinceFE "$\checkmark$"

{txt}added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

{com}. estadd local YearFE "$\times$"

{txt}added macro:
             e(YearFE) : "{res:$\times$}"

{com}. estadd local PeriodFE "$\times$"

{txt}added macro:
           e(PeriodFE) : "{res:$\times$}"

{com}. estadd local PeriodCtrols "$\times$"

{txt}added macro:
       e(PeriodCtrols) : "{res:$\times$}"

{com}. estadd local Data "Repl.Pkg"

{txt}added macro:
               e(Data) : "{res:Repl.Pkg}"

{com}. estadd local Sample "Pre-10"

{txt}added macro:
             e(Sample) : "{res:Pre-10}"

{com}. estadd local Propensity "$\times$"

{txt}added macro:
         e(Propensity) : "{res:$\times$}"

{com}. estadd local Estimation "OLS"

{txt}added macro:
         e(Estimation) : "{res:OLS}"

{com}. estadd local Outcome "Q1"

{txt}added macro:
            e(Outcome) : "{res:Q1}"

{com}. 
. ** NOte that this is province*year FE
. 
. * Run boottest to get the bootstrap confidence sets
. boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.8637
{col 25}{txt}Prob>|t| = {res}    0.4675

95%{txt} confidence set for null hypothesis expression: {res}[−.01615, .051]
{txt}
{com}. 
. gen sample_pkgQ1 = 1 if e(sample)==1
{txt}(181,296 missing values generated)

{com}. 
. rename (top_prizes_gdp_ours expenditure_gdp_ours) (top_prizes_gdp expenditure_gdp) // Just undoing the renaming 
{res}{txt}
{com}. 
. preserve
{txt}
{com}. 
. keep if sample_pkg == 1
{txt}(12,449 observations deleted)

{com}. keep survey
{txt}
{com}. duplicates drop 

{p 0 4}{txt}Duplicates in terms of {txt} all variables{p_end}

(282,707 observations deleted)

{com}. 
. tempfile survey_list
{txt}
{com}. save `survey_list'
{txt}{p 0 4 2}
file {bf}
/var/folders/54/7cxl8ny95nb4vs26j1q2yq9r0000gn/T//S_08037.000004{rm}
saved
as .dta format
{p_end}

{com}. 
. restore         
{txt}
{com}. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. ** Get list of surveys used in Replication package
. merge m:1 survey using `survey_list', gen(reppkg_surveys)
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}         844,201
{txt}{col 9}from master{col 30}{res}         844,201{txt}  (reppkg_surveys==1)
{col 9}from using{col 30}{res}               0{txt}  (reppkg_surveys==2)

{col 5}Matched{col 30}{res}         285,309{txt}  (reppkg_surveys==3)
{col 5}{hline 41}

{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. ** Period control:
. gen preperiod=1 if year_original<2010
{txt}(448,934 missing values generated)

{com}. replace preperiod=0 if year_original>=2010
{txt}(448,934 real changes made)

{com}. 
. global individual_characteristics "i.municipality_size female age i.education i.status"
{txt}
{com}. global ind_char_preperiod "preperiod#i.municipality_size preperiod#female preperiod#c.age preperiod#i.education preperiod#i.status"
{txt}
{com}. 
. ** Col 3, Appendix Table 19: 
. eststo b_preAll: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.year1 i.prov_num $individual_characteristics if year_original<2010, absorb(survey) 
{txt}{p 0 6 2}note: {bf:1987.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original#c.expenditure_gdp_ours} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1988.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year1} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:413,010}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:144}
{txt}{col 52}{lalign 17:F({res:87}, {res:412779})}{col 69} = {res}{ralign 7:135.36}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0376}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0371}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4375}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0027102{col 56}{space 2} .0040626{col 67}{space 1}    0.67{col 76}{space 3}0.505{col 84}{space 4}-.0052524{col 97}{space 3} .0106727
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.3497408{col 56}{space 2} .0917382{col 67}{space 1}   -3.81{col 76}{space 3}0.000{col 84}{space 4}-.5295449{col 97}{space 3}-.1699368
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1987  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1987  {c |}{col 44}{res}{space 2} .3135643{col 56}{space 2} .1486151{col 67}{space 1}    2.11{col 76}{space 3}0.035{col 84}{space 4} .0222832{col 97}{space 3} .6048453
{txt}{space 37}1988  {c |}{col 44}{res}{space 2} .2291567{col 56}{space 2} .1531403{col 67}{space 1}    1.50{col 76}{space 3}0.135{col 84}{space 4}-.0709936{col 97}{space 3} .5293071
{txt}{space 37}1989  {c |}{col 44}{res}{space 2} .1707434{col 56}{space 2} .1188838{col 67}{space 1}    1.44{col 76}{space 3}0.151{col 84}{space 4}-.0622653{col 97}{space 3} .4037521
{txt}{space 37}1990  {c |}{col 44}{res}{space 2}   .01865{col 56}{space 2} .1184159{col 67}{space 1}    0.16{col 76}{space 3}0.875{col 84}{space 4}-.2134416{col 97}{space 3} .2507416
{txt}{space 37}1991  {c |}{col 44}{res}{space 2} -.019187{col 56}{space 2} .1564434{col 67}{space 1}   -0.12{col 76}{space 3}0.902{col 84}{space 4}-.3258114{col 97}{space 3} .2874373
{txt}{space 37}1992  {c |}{col 44}{res}{space 2} .0489195{col 56}{space 2} .1255783{col 67}{space 1}    0.39{col 76}{space 3}0.697{col 84}{space 4}-.1972102{col 97}{space 3} .2950491
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}   .26664{col 56}{space 2} .1148674{col 67}{space 1}    2.32{col 76}{space 3}0.020{col 84}{space 4} .0415035{col 97}{space 3} .4917766
{txt}{space 37}1994  {c |}{col 44}{res}{space 2} .3136603{col 56}{space 2} .1171103{col 67}{space 1}    2.68{col 76}{space 3}0.007{col 84}{space 4} .0841276{col 97}{space 3} .5431931
{txt}{space 37}1995  {c |}{col 44}{res}{space 2} .2448395{col 56}{space 2} .1135223{col 67}{space 1}    2.16{col 76}{space 3}0.031{col 84}{space 4} .0223393{col 97}{space 3} .4673398
{txt}{space 37}1996  {c |}{col 44}{res}{space 2} .2793996{col 56}{space 2} .1269176{col 67}{space 1}    2.20{col 76}{space 3}0.028{col 84}{space 4}  .030645{col 97}{space 3} .5281542
{txt}{space 37}1997  {c |}{col 44}{res}{space 2} .7542858{col 56}{space 2} .1481706{col 67}{space 1}    5.09{col 76}{space 3}0.000{col 84}{space 4}  .463876{col 97}{space 3} 1.044696
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .6903791{col 56}{space 2} .1586371{col 67}{space 1}    4.35{col 76}{space 3}0.000{col 84}{space 4} .3794552{col 97}{space 3} 1.001303
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .6943459{col 56}{space 2} .1550547{col 67}{space 1}    4.48{col 76}{space 3}0.000{col 84}{space 4} .3904434{col 97}{space 3} .9982484
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .7562424{col 56}{space 2} .0972644{col 67}{space 1}    7.78{col 76}{space 3}0.000{col 84}{space 4} .5656071{col 97}{space 3} .9468776
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} .5852242{col 56}{space 2}  .146167{col 67}{space 1}    4.00{col 76}{space 3}0.000{col 84}{space 4} .2987413{col 97}{space 3}  .871707
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} .6629056{col 56}{space 2} .1498097{col 67}{space 1}    4.42{col 76}{space 3}0.000{col 84}{space 4} .3692831{col 97}{space 3} .9565281
{txt}{space 37}2003  {c |}{col 44}{res}{space 2} .4222332{col 56}{space 2} .1415158{col 67}{space 1}    2.98{col 76}{space 3}0.003{col 84}{space 4} .1448666{col 97}{space 3} .6995998
{txt}{space 37}2004  {c |}{col 44}{res}{space 2} .6825647{col 56}{space 2} .0961045{col 67}{space 1}    7.10{col 76}{space 3}0.000{col 84}{space 4} .4942028{col 97}{space 3} .8709266
{txt}{space 37}2005  {c |}{col 44}{res}{space 2} .1225662{col 56}{space 2} .1418336{col 67}{space 1}    0.86{col 76}{space 3}0.388{col 84}{space 4}-.1554233{col 97}{space 3} .4005557
{txt}{space 37}2006  {c |}{col 44}{res}{space 2}  .140964{col 56}{space 2} .1302095{col 67}{space 1}    1.08{col 76}{space 3}0.279{col 84}{space 4}-.1142426{col 97}{space 3} .3961705
{txt}{space 37}2007  {c |}{col 44}{res}{space 2} .3337424{col 56}{space 2} .1231779{col 67}{space 1}    2.71{col 76}{space 3}0.007{col 84}{space 4} .0923174{col 97}{space 3} .5751674
{txt}{space 37}2008  {c |}{col 44}{res}{space 2} .3318102{col 56}{space 2}  .094738{col 67}{space 1}    3.50{col 76}{space 3}0.000{col 84}{space 4} .1461265{col 97}{space 3} .5174939
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}year1 {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2010  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1052535{col 56}{space 2} .0079149{col 67}{space 1}  -13.30{col 76}{space 3}0.000{col 84}{space 4}-.1207665{col 97}{space 3}-.0897405
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0822943{col 56}{space 2} .0080567{col 67}{space 1}   10.21{col 76}{space 3}0.000{col 84}{space 4} .0665034{col 97}{space 3} .0980851
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0939738{col 56}{space 2} .0056484{col 67}{space 1}   16.64{col 76}{space 3}0.000{col 84}{space 4} .0829032{col 97}{space 3} .1050445
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2}  .078873{col 56}{space 2} .0072757{col 67}{space 1}   10.84{col 76}{space 3}0.000{col 84}{space 4} .0646129{col 97}{space 3} .0931331
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0055005{col 56}{space 2} .0057887{col 67}{space 1}    0.95{col 76}{space 3}0.342{col 84}{space 4}-.0058452{col 97}{space 3} .0168462
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0394109{col 56}{space 2} .0106825{col 67}{space 1}    3.69{col 76}{space 3}0.000{col 84}{space 4} .0184736{col 97}{space 3} .0603483
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .1156943{col 56}{space 2} .0067995{col 67}{space 1}   17.02{col 76}{space 3}0.000{col 84}{space 4} .1023675{col 97}{space 3} .1290211
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2} .0030778{col 56}{space 2} .0047257{col 67}{space 1}    0.65{col 76}{space 3}0.515{col 84}{space 4}-.0061844{col 97}{space 3}   .01234
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2}-.0232399{col 56}{space 2} .0077916{col 67}{space 1}   -2.98{col 76}{space 3}0.003{col 84}{space 4}-.0385113{col 97}{space 3}-.0079685
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .1049486{col 56}{space 2} .0074783{col 67}{space 1}   14.03{col 76}{space 3}0.000{col 84}{space 4} .0902914{col 97}{space 3} .1196058
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0739931{col 56}{space 2} .0060078{col 67}{space 1}   12.32{col 76}{space 3}0.000{col 84}{space 4}  .062218{col 97}{space 3} .0857682
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0572307{col 56}{space 2} .0070317{col 67}{space 1}    8.14{col 76}{space 3}0.000{col 84}{space 4} .0434488{col 97}{space 3} .0710126
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0952709{col 56}{space 2} .0071198{col 67}{space 1}   13.38{col 76}{space 3}0.000{col 84}{space 4} .0813163{col 97}{space 3} .1092254
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0825908{col 56}{space 2} .0072854{col 67}{space 1}   11.34{col 76}{space 3}0.000{col 84}{space 4} .0683116{col 97}{space 3} .0968699
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0140686{col 56}{space 2} .0064419{col 67}{space 1}   -2.18{col 76}{space 3}0.029{col 84}{space 4}-.0266945{col 97}{space 3}-.0014427
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0640787{col 56}{space 2} .0093011{col 67}{space 1}    6.89{col 76}{space 3}0.000{col 84}{space 4} .0458488{col 97}{space 3} .0823086
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1688208{col 56}{space 2} .0063054{col 67}{space 1}  -26.77{col 76}{space 3}0.000{col 84}{space 4}-.1811792{col 97}{space 3}-.1564624
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0607847{col 56}{space 2}  .007089{col 67}{space 1}   -8.57{col 76}{space 3}0.000{col 84}{space 4}-.0746789{col 97}{space 3}-.0468904
{txt}{space 34}granada  {c |}{col 44}{res}{space 2}   .05784{col 56}{space 2} .0064086{col 67}{space 1}    9.03{col 76}{space 3}0.000{col 84}{space 4} .0452793{col 97}{space 3} .0704006
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2}  .032025{col 56}{space 2} .0104043{col 67}{space 1}    3.08{col 76}{space 3}0.002{col 84}{space 4} .0116328{col 97}{space 3} .0524172
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0865994{col 56}{space 2} .0073685{col 67}{space 1}   11.75{col 76}{space 3}0.000{col 84}{space 4} .0721574{col 97}{space 3} .1010415
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0112883{col 56}{space 2} .0091932{col 67}{space 1}    1.23{col 76}{space 3}0.219{col 84}{space 4}  -.00673{col 97}{space 3} .0293067
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0499447{col 56}{space 2} .0065446{col 67}{space 1}    7.63{col 76}{space 3}0.000{col 84}{space 4} .0371176{col 97}{space 3} .0627719
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0475319{col 56}{space 2} .0065992{col 67}{space 1}    7.20{col 76}{space 3}0.000{col 84}{space 4} .0345978{col 97}{space 3} .0604661
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2}-.0095342{col 56}{space 2} .0086809{col 67}{space 1}   -1.10{col 76}{space 3}0.272{col 84}{space 4}-.0265485{col 97}{space 3} .0074802
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0595835{col 56}{space 2} .0063951{col 67}{space 1}    9.32{col 76}{space 3}0.000{col 84}{space 4} .0470492{col 97}{space 3} .0721178
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0305185{col 56}{space 2} .0070977{col 67}{space 1}    4.30{col 76}{space 3}0.000{col 84}{space 4} .0166072{col 97}{space 3} .0444298
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0569249{col 56}{space 2} .0079082{col 67}{space 1}   -7.20{col 76}{space 3}0.000{col 84}{space 4}-.0724247{col 97}{space 3}-.0414251
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}-.0004385{col 56}{space 2} .0074282{col 67}{space 1}   -0.06{col 76}{space 3}0.953{col 84}{space 4}-.0149975{col 97}{space 3} .0141206
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0277433{col 56}{space 2} .0049366{col 67}{space 1}    5.62{col 76}{space 3}0.000{col 84}{space 4} .0180677{col 97}{space 3} .0374188
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .1100199{col 56}{space 2} .0059685{col 67}{space 1}   18.43{col 76}{space 3}0.000{col 84}{space 4} .0983219{col 97}{space 3}  .121718
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0360062{col 56}{space 2} .0059258{col 67}{space 1}    6.08{col 76}{space 3}0.000{col 84}{space 4} .0243919{col 97}{space 3} .0476206
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0493216{col 56}{space 2} .0070022{col 67}{space 1}   -7.04{col 76}{space 3}0.000{col 84}{space 4}-.0630457{col 97}{space 3}-.0355976
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2}-.0062248{col 56}{space 2} .0075496{col 67}{space 1}   -0.82{col 76}{space 3}0.410{col 84}{space 4}-.0210219{col 97}{space 3} .0085722
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0296544{col 56}{space 2}  .009758{col 67}{space 1}    3.04{col 76}{space 3}0.002{col 84}{space 4}  .010529{col 97}{space 3} .0487798
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0214773{col 56}{space 2} .0060374{col 67}{space 1}   -3.56{col 76}{space 3}0.000{col 84}{space 4}-.0333105{col 97}{space 3}-.0096442
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0626491{col 56}{space 2} .0076467{col 67}{space 1}    8.19{col 76}{space 3}0.000{col 84}{space 4} .0476618{col 97}{space 3} .0776364
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0389976{col 56}{space 2} .0064331{col 67}{space 1}    6.06{col 76}{space 3}0.000{col 84}{space 4}  .026389{col 97}{space 3} .0516063
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0135028{col 56}{space 2} .0102111{col 67}{space 1}    1.32{col 76}{space 3}0.186{col 84}{space 4}-.0065107{col 97}{space 3} .0335163
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2}  .100337{col 56}{space 2} .0056563{col 67}{space 1}   17.74{col 76}{space 3}0.000{col 84}{space 4} .0892509{col 97}{space 3} .1114231
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0564155{col 56}{space 2}  .012648{col 67}{space 1}    4.46{col 76}{space 3}0.000{col 84}{space 4} .0316259{col 97}{space 3} .0812052
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0326571{col 56}{space 2} .0067798{col 67}{space 1}   -4.82{col 76}{space 3}0.000{col 84}{space 4}-.0459453{col 97}{space 3}-.0193689
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0419759{col 56}{space 2} .0097235{col 67}{space 1}    4.32{col 76}{space 3}0.000{col 84}{space 4} .0229182{col 97}{space 3} .0610336
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0278256{col 56}{space 2} .0072999{col 67}{space 1}    3.81{col 76}{space 3}0.000{col 84}{space 4}  .013518{col 97}{space 3} .0421332
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2}   .05172{col 56}{space 2} .0052785{col 67}{space 1}    9.80{col 76}{space 3}0.000{col 84}{space 4} .0413744{col 97}{space 3} .0620656
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0310382{col 56}{space 2} .0075648{col 67}{space 1}    4.10{col 76}{space 3}0.000{col 84}{space 4} .0162114{col 97}{space 3} .0458651
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2} -.143782{col 56}{space 2} .0056167{col 67}{space 1}  -25.60{col 76}{space 3}0.000{col 84}{space 4}-.1547905{col 97}{space 3}-.1327735
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0408254{col 56}{space 2} .0096601{col 67}{space 1}    4.23{col 76}{space 3}0.000{col 84}{space 4}  .021892{col 97}{space 3} .0597589
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0019454{col 56}{space 2} .0067785{col 67}{space 1}    0.29{col 76}{space 3}0.774{col 84}{space 4}-.0113402{col 97}{space 3} .0152311
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0001578{col 56}{space 2} .0029572{col 67}{space 1}   -0.05{col 76}{space 3}0.957{col 84}{space 4}-.0059539{col 97}{space 3} .0056383
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0044558{col 56}{space 2} .0029226{col 67}{space 1}    1.52{col 76}{space 3}0.127{col 84}{space 4}-.0012725{col 97}{space 3} .0101841
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} -.000905{col 56}{space 2} .0034113{col 67}{space 1}   -0.27{col 76}{space 3}0.791{col 84}{space 4}-.0075911{col 97}{space 3} .0057811
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}  .007249{col 56}{space 2} .0029847{col 67}{space 1}    2.43{col 76}{space 3}0.015{col 84}{space 4}  .001399{col 97}{space 3}  .013099
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0369115{col 56}{space 2} .0042982{col 67}{space 1}   -8.59{col 76}{space 3}0.000{col 84}{space 4}-.0453359{col 97}{space 3}-.0284872
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0135008{col 56}{space 2} .0039208{col 67}{space 1}   -3.44{col 76}{space 3}0.001{col 84}{space 4}-.0211853{col 97}{space 3}-.0058162
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0164743{col 56}{space 2}  .001588{col 67}{space 1}  -10.37{col 76}{space 3}0.000{col 84}{space 4}-.0195867{col 97}{space 3} -.013362
{txt}{space 39}age {c |}{col 44}{res}{space 2}  .000377{col 56}{space 2} .0000575{col 67}{space 1}    6.56{col 76}{space 3}0.000{col 84}{space 4} .0002643{col 97}{space 3} .0004897
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0370207{col 56}{space 2} .0019132{col 67}{space 1}  -19.35{col 76}{space 3}0.000{col 84}{space 4}-.0407706{col 97}{space 3}-.0332708
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0526771{col 56}{space 2}  .002253{col 67}{space 1}  -23.38{col 76}{space 3}0.000{col 84}{space 4} -.057093{col 97}{space 3}-.0482612
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0070597{col 56}{space 2} .0024466{col 67}{space 1}   -2.89{col 76}{space 3}0.004{col 84}{space 4} -.011855{col 97}{space 3}-.0022644
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0384215{col 56}{space 2} .0025465{col 67}{space 1}   15.09{col 76}{space 3}0.000{col 84}{space 4} .0334304{col 97}{space 3} .0434125
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0327871{col 56}{space 2} .0030338{col 67}{space 1}  -10.81{col 76}{space 3}0.000{col 84}{space 4}-.0387332{col 97}{space 3} -.026841
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0161312{col 56}{space 2} .0022855{col 67}{space 1}    7.06{col 76}{space 3}0.000{col 84}{space 4} .0116517{col 97}{space 3} .0206106
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2427086{col 56}{space 2} .0058165{col 67}{space 1}   41.73{col 76}{space 3}0.000{col 84}{space 4} .2313084{col 97}{space 3} .2541088
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}143{txt}, {res}412779{txt}) = {res}27.105{col 62}{txt} Prob > F = {res}0.000
{txt}
{com}. estadd local SurveyFE "$\checkmark$"

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

{com}. estadd local ProvinceFE "$\checkmark$"

{txt}added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

{com}. estadd local YearFE "$\checkmark$"

{txt}added macro:
             e(YearFE) : "{res:$\checkmark$}"

{com}. estadd local PeriodFE "$\times$"

{txt}added macro:
           e(PeriodFE) : "{res:$\times$}"

{com}. estadd local PeriodCtrols "$\times$"

{txt}added macro:
       e(PeriodCtrols) : "{res:$\times$}"

{com}. estadd local Data "Ours"

{txt}added macro:
               e(Data) : "{res:Ours}"

{com}. estadd local Sample "Pre-10"

{txt}added macro:
             e(Sample) : "{res:Pre-10}"

{com}. estadd local Propensity "$\checkmark$"

{txt}added macro:
         e(Propensity) : "{res:$\checkmark$}"

{com}. estadd local Estimation "OLS"

{txt}added macro:
         e(Estimation) : "{res:OLS}"

{com}. estadd local Outcome "All year"

{txt}added macro:
            e(Outcome) : "{res:All year}"

{com}. 
. boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.2816
{col 25}{txt}Prob>|t| = {res}    0.7678

95%{txt} confidence set for null hypothesis expression: {res}[−.02257, .02806]
{txt}
{com}. 
. ** Col 4, Appendix Table 19: 
. eststo b_postAll: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.year1 i.prov_num $individual_characteristics if year_original>=2010, absorb(survey) 
{txt}{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year1} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2023.year1} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:289,178}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:82}
{txt}{col 52}{lalign 17:F({res:77}, {res:289019})}{col 69} = {res}{ralign 7:80.44}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0306}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0301}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.3966}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0048264{col 56}{space 2}  .004472{col 67}{space 1}   -1.08{col 76}{space 3}0.280{col 84}{space 4}-.0135914{col 97}{space 3} .0039386
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.2563617{col 56}{space 2} .0945291{col 67}{space 1}   -2.71{col 76}{space 3}0.007{col 84}{space 4}-.4416362{col 97}{space 3}-.0710872
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2} .0483102{col 56}{space 2} .1419683{col 67}{space 1}    0.34{col 76}{space 3}0.734{col 84}{space 4}-.2299437{col 97}{space 3}  .326564
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .7598778{col 56}{space 2} .1411477{col 67}{space 1}    5.38{col 76}{space 3}0.000{col 84}{space 4} .4832323{col 97}{space 3} 1.036523
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .5141395{col 56}{space 2} .1336449{col 67}{space 1}    3.85{col 76}{space 3}0.000{col 84}{space 4} .2521992{col 97}{space 3} .7760798
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .4505118{col 56}{space 2} .1451562{col 67}{space 1}    3.10{col 76}{space 3}0.002{col 84}{space 4} .1660097{col 97}{space 3} .7350139
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} .6065019{col 56}{space 2} .1459337{col 67}{space 1}    4.16{col 76}{space 3}0.000{col 84}{space 4} .3204759{col 97}{space 3} .8925279
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .4440725{col 56}{space 2} .1304288{col 67}{space 1}    3.40{col 76}{space 3}0.001{col 84}{space 4} .1884356{col 97}{space 3} .6997094
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .6370715{col 56}{space 2} .1354039{col 67}{space 1}    4.70{col 76}{space 3}0.000{col 84}{space 4} .3716837{col 97}{space 3} .9024593
{txt}{space 37}2018  {c |}{col 44}{res}{space 2} .5126373{col 56}{space 2} .1366839{col 67}{space 1}    3.75{col 76}{space 3}0.000{col 84}{space 4} .2447407{col 97}{space 3}  .780534
{txt}{space 37}2019  {c |}{col 44}{res}{space 2} .1546613{col 56}{space 2} .0984668{col 67}{space 1}    1.57{col 76}{space 3}0.116{col 84}{space 4} -.038331{col 97}{space 3} .3476535
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .2624584{col 56}{space 2} .1057219{col 67}{space 1}    2.48{col 76}{space 3}0.013{col 84}{space 4} .0552464{col 97}{space 3} .4696703
{txt}{space 37}2021  {c |}{col 44}{res}{space 2} .0645481{col 56}{space 2} .1090411{col 67}{space 1}    0.59{col 76}{space 3}0.554{col 84}{space 4}-.1491694{col 97}{space 3} .2782656
{txt}{space 37}2022  {c |}{col 44}{res}{space 2} .1111422{col 56}{space 2} .1030539{col 67}{space 1}    1.08{col 76}{space 3}0.281{col 84}{space 4}-.0908406{col 97}{space 3}  .313125
{txt}{space 42} {c |}
{space 37}year1 {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2023  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.0556249{col 56}{space 2} .0091916{col 67}{space 1}   -6.05{col 76}{space 3}0.000{col 84}{space 4}-.0736402{col 97}{space 3}-.0376095
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0097263{col 56}{space 2} .0090785{col 67}{space 1}    1.07{col 76}{space 3}0.284{col 84}{space 4}-.0080672{col 97}{space 3} .0275199
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2}  .025382{col 56}{space 2} .0059941{col 67}{space 1}    4.23{col 76}{space 3}0.000{col 84}{space 4} .0136338{col 97}{space 3} .0371302
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0174605{col 56}{space 2} .0075513{col 67}{space 1}    2.31{col 76}{space 3}0.021{col 84}{space 4} .0026601{col 97}{space 3} .0322608
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2}-.0136643{col 56}{space 2} .0065067{col 67}{space 1}   -2.10{col 76}{space 3}0.036{col 84}{space 4}-.0264171{col 97}{space 3}-.0009114
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}-.0032753{col 56}{space 2} .0109828{col 67}{space 1}   -0.30{col 76}{space 3}0.766{col 84}{space 4}-.0248013{col 97}{space 3} .0182507
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0549477{col 56}{space 2}  .007376{col 67}{space 1}    7.45{col 76}{space 3}0.000{col 84}{space 4}  .040491{col 97}{space 3} .0694045
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0413194{col 56}{space 2} .0051997{col 67}{space 1}   -7.95{col 76}{space 3}0.000{col 84}{space 4}-.0515108{col 97}{space 3}-.0311281
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0104838{col 56}{space 2} .0091527{col 67}{space 1}    1.15{col 76}{space 3}0.252{col 84}{space 4}-.0074551{col 97}{space 3} .0284228
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0537891{col 56}{space 2} .0087569{col 67}{space 1}    6.14{col 76}{space 3}0.000{col 84}{space 4} .0366259{col 97}{space 3} .0709524
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} -.011986{col 56}{space 2} .0064045{col 67}{space 1}   -1.87{col 76}{space 3}0.061{col 84}{space 4}-.0245386{col 97}{space 3} .0005667
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0026217{col 56}{space 2} .0070981{col 67}{space 1}    0.37{col 76}{space 3}0.712{col 84}{space 4}-.0112903{col 97}{space 3} .0165337
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2}-.0012444{col 56}{space 2} .0080492{col 67}{space 1}   -0.15{col 76}{space 3}0.877{col 84}{space 4}-.0170207{col 97}{space 3} .0145318
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2}  .001726{col 56}{space 2} .0086066{col 67}{space 1}    0.20{col 76}{space 3}0.841{col 84}{space 4}-.0151426{col 97}{space 3} .0185947
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0161118{col 56}{space 2} .0074577{col 67}{space 1}   -2.16{col 76}{space 3}0.031{col 84}{space 4}-.0307286{col 97}{space 3} -.001495
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0169869{col 56}{space 2} .0104793{col 67}{space 1}    1.62{col 76}{space 3}0.105{col 84}{space 4}-.0035523{col 97}{space 3} .0375261
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.0934715{col 56}{space 2} .0072867{col 67}{space 1}  -12.83{col 76}{space 3}0.000{col 84}{space 4}-.1077532{col 97}{space 3}-.0791898
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0784512{col 56}{space 2}  .007406{col 67}{space 1}  -10.59{col 76}{space 3}0.000{col 84}{space 4}-.0929669{col 97}{space 3}-.0639356
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0162561{col 56}{space 2} .0068608{col 67}{space 1}    2.37{col 76}{space 3}0.018{col 84}{space 4} .0028091{col 97}{space 3} .0297032
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0239415{col 56}{space 2} .0101025{col 67}{space 1}    2.37{col 76}{space 3}0.018{col 84}{space 4} .0041408{col 97}{space 3} .0437422
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0222971{col 56}{space 2} .0083376{col 67}{space 1}    2.67{col 76}{space 3}0.007{col 84}{space 4} .0059555{col 97}{space 3} .0386386
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0108469{col 56}{space 2} .0107319{col 67}{space 1}    1.01{col 76}{space 3}0.312{col 84}{space 4}-.0101872{col 97}{space 3} .0318811
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}-.0017128{col 56}{space 2} .0067769{col 67}{space 1}   -0.25{col 76}{space 3}0.800{col 84}{space 4}-.0149953{col 97}{space 3} .0115698
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0140461{col 56}{space 2} .0075747{col 67}{space 1}    1.85{col 76}{space 3}0.064{col 84}{space 4}-.0008001{col 97}{space 3} .0288923
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0239522{col 56}{space 2}  .007875{col 67}{space 1}    3.04{col 76}{space 3}0.002{col 84}{space 4} .0085174{col 97}{space 3} .0393871
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0240841{col 56}{space 2} .0067402{col 67}{space 1}    3.57{col 76}{space 3}0.000{col 84}{space 4} .0108735{col 97}{space 3} .0372948
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0162647{col 56}{space 2} .0082575{col 67}{space 1}    1.97{col 76}{space 3}0.049{col 84}{space 4} .0000803{col 97}{space 3} .0324491
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0823993{col 56}{space 2} .0083657{col 67}{space 1}   -9.85{col 76}{space 3}0.000{col 84}{space 4}-.0987958{col 97}{space 3}-.0660028
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}-.0061128{col 56}{space 2} .0092479{col 67}{space 1}   -0.66{col 76}{space 3}0.509{col 84}{space 4}-.0242385{col 97}{space 3} .0120129
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0055978{col 56}{space 2} .0052914{col 67}{space 1}    1.06{col 76}{space 3}0.290{col 84}{space 4}-.0047731{col 97}{space 3} .0159687
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0225995{col 56}{space 2} .0063851{col 67}{space 1}    3.54{col 76}{space 3}0.000{col 84}{space 4} .0100849{col 97}{space 3}  .035114
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2}   .00101{col 56}{space 2} .0063961{col 67}{space 1}    0.16{col 76}{space 3}0.875{col 84}{space 4}-.0115262{col 97}{space 3} .0135462
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0374961{col 56}{space 2} .0071774{col 67}{space 1}   -5.22{col 76}{space 3}0.000{col 84}{space 4}-.0515637{col 97}{space 3}-.0234285
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0255392{col 56}{space 2} .0093559{col 67}{space 1}    2.73{col 76}{space 3}0.006{col 84}{space 4}  .007202{col 97}{space 3} .0438765
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2}  .008929{col 56}{space 2} .0110185{col 67}{space 1}    0.81{col 76}{space 3}0.418{col 84}{space 4}-.0126669{col 97}{space 3} .0305249
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} .0039073{col 56}{space 2} .0067982{col 67}{space 1}    0.57{col 76}{space 3}0.565{col 84}{space 4} -.009417{col 97}{space 3} .0172317
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0029302{col 56}{space 2} .0092345{col 67}{space 1}    0.32{col 76}{space 3}0.751{col 84}{space 4}-.0151693{col 97}{space 3} .0210296
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0407944{col 56}{space 2} .0069576{col 67}{space 1}    5.86{col 76}{space 3}0.000{col 84}{space 4} .0271577{col 97}{space 3} .0544311
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0170718{col 56}{space 2} .0115858{col 67}{space 1}    1.47{col 76}{space 3}0.141{col 84}{space 4}-.0056361{col 97}{space 3} .0397797
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2}  .007714{col 56}{space 2} .0061101{col 67}{space 1}    1.26{col 76}{space 3}0.207{col 84}{space 4}-.0042616{col 97}{space 3} .0196895
{txt}{space 36}soria  {c |}{col 44}{res}{space 2}  .025672{col 56}{space 2} .0143401{col 67}{space 1}    1.79{col 76}{space 3}0.073{col 84}{space 4}-.0024343{col 97}{space 3} .0537783
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0513686{col 56}{space 2} .0072618{col 67}{space 1}   -7.07{col 76}{space 3}0.000{col 84}{space 4}-.0656015{col 97}{space 3}-.0371357
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0238901{col 56}{space 2}  .012032{col 67}{space 1}   -1.99{col 76}{space 3}0.047{col 84}{space 4}-.0474724{col 97}{space 3}-.0003077
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0211887{col 56}{space 2} .0075078{col 67}{space 1}    2.82{col 76}{space 3}0.005{col 84}{space 4} .0064736{col 97}{space 3} .0359038
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2}-.0035948{col 56}{space 2} .0057939{col 67}{space 1}   -0.62{col 76}{space 3}0.535{col 84}{space 4}-.0149507{col 97}{space 3}  .007761
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}-.0086468{col 56}{space 2} .0078683{col 67}{space 1}   -1.10{col 76}{space 3}0.272{col 84}{space 4}-.0240683{col 97}{space 3} .0067748
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.0806907{col 56}{space 2} .0065341{col 67}{space 1}  -12.35{col 76}{space 3}0.000{col 84}{space 4}-.0934974{col 97}{space 3}-.0678839
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0334326{col 56}{space 2} .0116551{col 67}{space 1}    2.87{col 76}{space 3}0.004{col 84}{space 4}  .010589{col 97}{space 3} .0562762
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2}-.0029762{col 56}{space 2} .0072282{col 67}{space 1}   -0.41{col 76}{space 3}0.681{col 84}{space 4}-.0171433{col 97}{space 3} .0111908
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0059255{col 56}{space 2} .0034874{col 67}{space 1}    1.70{col 76}{space 3}0.089{col 84}{space 4}-.0009098{col 97}{space 3} .0127608
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0034283{col 56}{space 2} .0033624{col 67}{space 1}    1.02{col 76}{space 3}0.308{col 84}{space 4} -.003162{col 97}{space 3} .0100186
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0067558{col 56}{space 2} .0037164{col 67}{space 1}    1.82{col 76}{space 3}0.069{col 84}{space 4}-.0005283{col 97}{space 3} .0140399
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0089386{col 56}{space 2} .0034347{col 67}{space 1}    2.60{col 76}{space 3}0.009{col 84}{space 4} .0022067{col 97}{space 3} .0156706
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}  .000234{col 56}{space 2} .0046329{col 67}{space 1}    0.05{col 76}{space 3}0.960{col 84}{space 4}-.0088464{col 97}{space 3} .0093144
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}  .010373{col 56}{space 2}  .004459{col 67}{space 1}    2.33{col 76}{space 3}0.020{col 84}{space 4} .0016336{col 97}{space 3} .0191125
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0110966{col 56}{space 2} .0015269{col 67}{space 1}    7.27{col 76}{space 3}0.000{col 84}{space 4} .0081039{col 97}{space 3} .0140892
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0016007{col 56}{space 2} .0000688{col 67}{space 1}   23.28{col 76}{space 3}0.000{col 84}{space 4}  .001466{col 97}{space 3} .0017355
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0253383{col 56}{space 2} .0019263{col 67}{space 1}  -13.15{col 76}{space 3}0.000{col 84}{space 4}-.0291138{col 97}{space 3}-.0215627
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0383896{col 56}{space 2} .0020626{col 67}{space 1}  -18.61{col 76}{space 3}0.000{col 84}{space 4}-.0424323{col 97}{space 3}-.0343469
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0031572{col 56}{space 2} .0022664{col 67}{space 1}    1.39{col 76}{space 3}0.164{col 84}{space 4}-.0012849{col 97}{space 3} .0075993
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0381336{col 56}{space 2} .0026014{col 67}{space 1}   14.66{col 76}{space 3}0.000{col 84}{space 4}  .033035{col 97}{space 3} .0432323
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0181727{col 56}{space 2} .0040184{col 67}{space 1}    4.52{col 76}{space 3}0.000{col 84}{space 4} .0102967{col 97}{space 3} .0260486
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}  .017599{col 56}{space 2} .0036767{col 67}{space 1}    4.79{col 76}{space 3}0.000{col 84}{space 4} .0103928{col 97}{space 3} .0248053
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1259478{col 56}{space 2} .0067394{col 67}{space 1}   18.69{col 76}{space 3}0.000{col 84}{space 4} .1127387{col 97}{space 3} .1391568
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}81{txt}, {res}289019{txt}) = {res}31.697{col 62}{txt} Prob > F = {res}0.000
{txt}
{com}. 
. estadd local SurveyFE "$\checkmark$"

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

{com}. estadd local ProvinceFE "$\checkmark$"

{txt}added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

{com}. estadd local YearFE "$\checkmark$"

{txt}added macro:
             e(YearFE) : "{res:$\checkmark$}"

{com}. estadd local PeriodFE "$\times$"

{txt}added macro:
           e(PeriodFE) : "{res:$\times$}"

{com}. estadd local PeriodCtrols "$\times$"

{txt}added macro:
       e(PeriodCtrols) : "{res:$\times$}"

{com}. estadd local Data "Ours"

{txt}added macro:
               e(Data) : "{res:Ours}"

{com}. estadd local Sample "Post-09"

{txt}added macro:
             e(Sample) : "{res:Post-09}"

{com}. estadd local Propensity "$\checkmark$"

{txt}added macro:
         e(Propensity) : "{res:$\checkmark$}"

{com}. estadd local Estimation "OLS"

{txt}added macro:
         e(Estimation) : "{res:OLS}"

{com}. estadd local Outcome "All year"

{txt}added macro:
            e(Outcome) : "{res:All year}"

{com}. 
. boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.9103
{col 25}{txt}Prob>|t| = {res}    0.4244

95%{txt} confidence set for null hypothesis expression: {res}[−.02115, .006868]
{txt}
{com}. 
. ** Col 5, Appendix Table 19:  
. eststo b_pooledAll: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num preperiod $individual_characteristics $ind_char_preperiod i.prov_num#preperiod, absorb(survey)  
{txt}{p 0 6 2}note: {bf:1987.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original#c.expenditure_gdp_ours} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#0b.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#1.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#3.education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:702,188}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:226}
{txt}{col 52}{lalign 17:F({res:163}, {res:701799})}{col 69} = {res}{ralign 7:111.66}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0413}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0407}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4211}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2}-.0003355{col 58}{space 2} .0030188{col 69}{space 1}   -0.11{col 78}{space 3}0.912{col 86}{space 4}-.0062522{col 99}{space 3} .0055812
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2} -.143647{col 58}{space 2} .0453484{col 69}{space 1}   -3.17{col 78}{space 3}0.002{col 86}{space 4}-.2325285{col 99}{space 3}-.0547656
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1987  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1987  {c |}{col 46}{res}{space 2} .1077313{col 58}{space 2} .1216071{col 69}{space 1}    0.89{col 78}{space 3}0.376{col 86}{space 4}-.1306147{col 99}{space 3} .3460774
{txt}{space 39}1988  {c |}{col 46}{res}{space 2} .0250739{col 58}{space 2}  .126652{col 69}{space 1}    0.20{col 78}{space 3}0.843{col 86}{space 4}-.2231599{col 99}{space 3} .2733077
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.0338473{col 58}{space 2}  .086107{col 69}{space 1}   -0.39{col 78}{space 3}0.694{col 86}{space 4}-.2026142{col 99}{space 3} .1349197
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.1855589{col 58}{space 2}  .085534{col 69}{space 1}   -2.17{col 78}{space 3}0.030{col 86}{space 4}-.3532027{col 99}{space 3}-.0179151
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}-.2227901{col 58}{space 2} .1304164{col 69}{space 1}   -1.71{col 78}{space 3}0.088{col 86}{space 4} -.478402{col 99}{space 3} .0328218
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}-.1549008{col 58}{space 2} .0945451{col 69}{space 1}   -1.64{col 78}{space 3}0.101{col 86}{space 4}-.3402061{col 99}{space 3} .0304044
{txt}{space 39}1993  {c |}{col 46}{res}{space 2} .0602905{col 58}{space 2} .0810623{col 69}{space 1}    0.74{col 78}{space 3}0.457{col 86}{space 4} -.098589{col 99}{space 3} .2191699
{txt}{space 39}1994  {c |}{col 46}{res}{space 2} .1080685{col 58}{space 2} .0839496{col 69}{space 1}    1.29{col 78}{space 3}0.198{col 86}{space 4}  -.05647{col 99}{space 3} .2726069
{txt}{space 39}1995  {c |}{col 46}{res}{space 2} .0399876{col 58}{space 2} .0792918{col 69}{space 1}    0.50{col 78}{space 3}0.614{col 86}{space 4}-.1154217{col 99}{space 3}  .195397
{txt}{space 39}1996  {c |}{col 46}{res}{space 2} .0705288{col 58}{space 2} .0963462{col 69}{space 1}    0.73{col 78}{space 3}0.464{col 86}{space 4}-.1183067{col 99}{space 3} .2593642
{txt}{space 39}1997  {c |}{col 46}{res}{space 2} .5527477{col 58}{space 2}  .121227{col 69}{space 1}    4.56{col 78}{space 3}0.000{col 86}{space 4} .3151466{col 99}{space 3} .7903487
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .4854892{col 58}{space 2}  .132979{col 69}{space 1}    3.65{col 78}{space 3}0.000{col 86}{space 4} .2248547{col 99}{space 3} .7461237
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .4860552{col 58}{space 2} .1291167{col 69}{space 1}    3.76{col 78}{space 3}0.000{col 86}{space 4} .2329907{col 99}{space 3} .7391196
{txt}{space 39}2000  {c |}{col 46}{res}{space 2} .5514445{col 58}{space 2} .0561442{col 69}{space 1}    9.82{col 78}{space 3}0.000{col 86}{space 4} .4414037{col 99}{space 3} .6614853
{txt}{space 39}2001  {c |}{col 46}{res}{space 2} .3830564{col 58}{space 2} .1189824{col 69}{space 1}    3.22{col 78}{space 3}0.001{col 86}{space 4} .1498548{col 99}{space 3} .6162581
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .4567489{col 58}{space 2} .1232221{col 69}{space 1}    3.71{col 78}{space 3}0.000{col 86}{space 4} .2152376{col 99}{space 3} .6982601
{txt}{space 39}2003  {c |}{col 46}{res}{space 2} .2184428{col 58}{space 2} .1137195{col 69}{space 1}    1.92{col 78}{space 3}0.055{col 86}{space 4}-.0044436{col 99}{space 3} .4413293
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} .4786774{col 58}{space 2} .0541883{col 69}{space 1}    8.83{col 78}{space 3}0.000{col 86}{space 4}   .37247{col 99}{space 3} .5848847
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}-.0783942{col 58}{space 2} .1140335{col 69}{space 1}   -0.69{col 78}{space 3}0.492{col 86}{space 4}-.3018962{col 99}{space 3} .1451079
{txt}{space 39}2006  {c |}{col 46}{res}{space 2}-.0661648{col 58}{space 2} .1004416{col 69}{space 1}   -0.66{col 78}{space 3}0.510{col 86}{space 4}-.2630271{col 99}{space 3} .1306975
{txt}{space 39}2007  {c |}{col 46}{res}{space 2} .1272149{col 58}{space 2}  .091826{col 69}{space 1}    1.39{col 78}{space 3}0.166{col 86}{space 4} -.052761{col 99}{space 3} .3071908
{txt}{space 39}2008  {c |}{col 46}{res}{space 2} .1259339{col 58}{space 2}  .051829{col 69}{space 1}    2.43{col 78}{space 3}0.015{col 86}{space 4} .0243507{col 99}{space 3}  .227517
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.2075159{col 58}{space 2} .0992485{col 69}{space 1}   -2.09{col 78}{space 3}0.037{col 86}{space 4}-.4020397{col 99}{space 3}-.0129922
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}  -.11118{col 58}{space 2} .1094306{col 69}{space 1}   -1.02{col 78}{space 3}0.310{col 86}{space 4}-.3256604{col 99}{space 3} .1033004
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.0641647{col 58}{space 2}  .121433{col 69}{space 1}   -0.53{col 78}{space 3}0.597{col 86}{space 4}-.3021694{col 99}{space 3} .1738399
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .6455495{col 58}{space 2} .1203493{col 69}{space 1}    5.36{col 78}{space 3}0.000{col 86}{space 4} .4096687{col 99}{space 3} .8814303
{txt}{space 39}2013  {c |}{col 46}{res}{space 2} .3986299{col 58}{space 2} .1100978{col 69}{space 1}    3.62{col 78}{space 3}0.000{col 86}{space 4} .1828417{col 99}{space 3}  .614418
{txt}{space 39}2014  {c |}{col 46}{res}{space 2} .3391199{col 58}{space 2} .1256298{col 69}{space 1}    2.70{col 78}{space 3}0.007{col 86}{space 4} .0928896{col 99}{space 3} .5853502
{txt}{space 39}2015  {c |}{col 46}{res}{space 2} .4960115{col 58}{space 2} .1265962{col 69}{space 1}    3.92{col 78}{space 3}0.000{col 86}{space 4} .2478872{col 99}{space 3} .7441358
{txt}{space 39}2016  {c |}{col 46}{res}{space 2} .3318631{col 58}{space 2} .1056421{col 69}{space 1}    3.14{col 78}{space 3}0.002{col 86}{space 4} .1248081{col 99}{space 3} .5389181
{txt}{space 39}2017  {c |}{col 46}{res}{space 2} .5254446{col 58}{space 2} .1125527{col 69}{space 1}    4.67{col 78}{space 3}0.000{col 86}{space 4}  .304845{col 99}{space 3} .7460443
{txt}{space 39}2018  {c |}{col 46}{res}{space 2} .3958699{col 58}{space 2} .1141998{col 69}{space 1}    3.47{col 78}{space 3}0.001{col 86}{space 4}  .172042{col 99}{space 3} .6196979
{txt}{space 39}2019  {c |}{col 46}{res}{space 2} .0379868{col 58}{space 2} .0537385{col 69}{space 1}    0.71{col 78}{space 3}0.480{col 86}{space 4}-.0673389{col 99}{space 3} .1433125
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}  .151196{col 58}{space 2} .0678224{col 69}{space 1}    2.23{col 78}{space 3}0.026{col 86}{space 4} .0182663{col 99}{space 3} .2841258
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.0464716{col 58}{space 2} .0732903{col 69}{space 1}   -0.63{col 78}{space 3}0.526{col 86}{space 4}-.1901181{col 99}{space 3} .0971749
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.0556387{col 58}{space 2} .0097604{col 69}{space 1}   -5.70{col 78}{space 3}0.000{col 86}{space 4}-.0747687{col 99}{space 3}-.0365086
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2} .0084811{col 58}{space 2} .0095865{col 69}{space 1}    0.88{col 78}{space 3}0.376{col 86}{space 4}-.0103081{col 99}{space 3} .0272703
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0252988{col 58}{space 2} .0063646{col 69}{space 1}    3.97{col 78}{space 3}0.000{col 86}{space 4} .0128244{col 99}{space 3} .0377732
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0172942{col 58}{space 2} .0080174{col 69}{space 1}    2.16{col 78}{space 3}0.031{col 86}{space 4} .0015803{col 99}{space 3}  .033008
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2}-.0136344{col 58}{space 2} .0069092{col 69}{space 1}   -1.97{col 78}{space 3}0.048{col 86}{space 4}-.0271762{col 99}{space 3}-.0000925
{txt}{space 38}avila  {c |}{col 46}{res}{space 2}-.0031717{col 58}{space 2} .0116621{col 69}{space 1}   -0.27{col 78}{space 3}0.786{col 86}{space 4} -.026029{col 99}{space 3} .0196856
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0549372{col 58}{space 2} .0078324{col 69}{space 1}    7.01{col 78}{space 3}0.000{col 86}{space 4} .0395859{col 99}{space 3} .0702885
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2} -.041326{col 58}{space 2} .0055215{col 69}{space 1}   -7.48{col 78}{space 3}0.000{col 86}{space 4} -.052148{col 99}{space 3}-.0305041
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0103665{col 58}{space 2} .0097185{col 69}{space 1}    1.07{col 78}{space 3}0.286{col 86}{space 4}-.0086815{col 99}{space 3} .0294145
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2}  .053822{col 58}{space 2} .0092987{col 69}{space 1}    5.79{col 78}{space 3}0.000{col 86}{space 4} .0355969{col 99}{space 3} .0720472
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2}-.0120302{col 58}{space 2} .0068007{col 69}{space 1}   -1.77{col 78}{space 3}0.077{col 86}{space 4}-.0253594{col 99}{space 3} .0012989
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2} .0026128{col 58}{space 2} .0075373{col 69}{space 1}    0.35{col 78}{space 3}0.729{col 86}{space 4}  -.01216{col 99}{space 3} .0173856
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2}-.0012367{col 58}{space 2} .0085473{col 69}{space 1}   -0.14{col 78}{space 3}0.885{col 86}{space 4}-.0179891{col 99}{space 3} .0155156
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0017693{col 58}{space 2} .0091391{col 69}{space 1}    0.19{col 78}{space 3}0.846{col 86}{space 4} -.016143{col 99}{space 3} .0196816
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2}-.0160828{col 58}{space 2} .0079191{col 69}{space 1}   -2.03{col 78}{space 3}0.042{col 86}{space 4}-.0316039{col 99}{space 3}-.0005616
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2} .0158261{col 58}{space 2} .0110873{col 69}{space 1}    1.43{col 78}{space 3}0.153{col 86}{space 4}-.0059047{col 99}{space 3} .0375569
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.0934761{col 58}{space 2} .0077376{col 69}{space 1}  -12.08{col 78}{space 3}0.000{col 86}{space 4}-.1086414{col 99}{space 3}-.0783107
{txt}{space 37}girona  {c |}{col 46}{res}{space 2}-.0784495{col 58}{space 2} .0078643{col 69}{space 1}   -9.98{col 78}{space 3}0.000{col 86}{space 4}-.0938632{col 99}{space 3}-.0630357
{txt}{space 36}granada  {c |}{col 46}{res}{space 2} .0161391{col 58}{space 2} .0072847{col 69}{space 1}    2.22{col 78}{space 3}0.027{col 86}{space 4} .0018613{col 99}{space 3} .0304169
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0239889{col 58}{space 2} .0107276{col 69}{space 1}    2.24{col 78}{space 3}0.025{col 86}{space 4} .0029632{col 99}{space 3} .0450147
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2} .0219995{col 58}{space 2} .0088502{col 69}{space 1}    2.49{col 78}{space 3}0.013{col 86}{space 4} .0046534{col 99}{space 3} .0393456
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2} .0094473{col 58}{space 2} .0113385{col 69}{space 1}    0.83{col 78}{space 3}0.405{col 86}{space 4}-.0127758{col 99}{space 3} .0316704
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2}-.0017058{col 58}{space 2} .0071962{col 69}{space 1}   -0.24{col 78}{space 3}0.813{col 86}{space 4}-.0158102{col 99}{space 3} .0123986
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0140681{col 58}{space 2} .0080434{col 69}{space 1}    1.75{col 78}{space 3}0.080{col 86}{space 4}-.0016966{col 99}{space 3} .0298329
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0239689{col 58}{space 2} .0083623{col 69}{space 1}    2.87{col 78}{space 3}0.004{col 86}{space 4}  .007579{col 99}{space 3} .0403587
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} .0239282{col 58}{space 2} .0071562{col 69}{space 1}    3.34{col 78}{space 3}0.001{col 86}{space 4} .0099024{col 99}{space 3} .0379541
{txt}{space 39}leon  {c |}{col 46}{res}{space 2} .0163231{col 58}{space 2} .0087683{col 69}{space 1}    1.86{col 78}{space 3}0.063{col 86}{space 4}-.0008624{col 99}{space 3} .0335087
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2}-.0823804{col 58}{space 2} .0088833{col 69}{space 1}   -9.27{col 78}{space 3}0.000{col 86}{space 4}-.0997914{col 99}{space 3}-.0649694
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2} -.006497{col 58}{space 2} .0098152{col 69}{space 1}   -0.66{col 78}{space 3}0.508{col 86}{space 4}-.0257344{col 99}{space 3} .0127404
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2} .0054953{col 58}{space 2} .0056182{col 69}{space 1}    0.98{col 78}{space 3}0.328{col 86}{space 4}-.0055161{col 99}{space 3} .0165067
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0225949{col 58}{space 2} .0067801{col 69}{space 1}    3.33{col 78}{space 3}0.001{col 86}{space 4} .0093061{col 99}{space 3} .0358838
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2}  .001017{col 58}{space 2} .0067919{col 69}{space 1}    0.15{col 78}{space 3}0.881{col 86}{space 4}-.0122949{col 99}{space 3} .0143288
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0375102{col 58}{space 2} .0076216{col 69}{space 1}   -4.92{col 78}{space 3}0.000{col 86}{space 4}-.0524482{col 99}{space 3}-.0225722
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} .0255553{col 58}{space 2} .0099348{col 69}{space 1}    2.57{col 78}{space 3}0.010{col 86}{space 4} .0060835{col 99}{space 3} .0450272
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2} .0090322{col 58}{space 2}    .0117{col 69}{space 1}    0.77{col 78}{space 3}0.440{col 86}{space 4}-.0138993{col 99}{space 3} .0319638
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2} .0039191{col 58}{space 2} .0072189{col 69}{space 1}    0.54{col 78}{space 3}0.587{col 86}{space 4}-.0102297{col 99}{space 3} .0180679
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2}  .002769{col 58}{space 2} .0098051{col 69}{space 1}    0.28{col 78}{space 3}0.778{col 86}{space 4}-.0164486{col 99}{space 3} .0219866
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .0407455{col 58}{space 2}  .007388{col 69}{space 1}    5.52{col 78}{space 3}0.000{col 86}{space 4} .0262653{col 99}{space 3} .0552258
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2} .0172329{col 58}{space 2}  .012302{col 69}{space 1}    1.40{col 78}{space 3}0.161{col 86}{space 4}-.0068787{col 99}{space 3} .0413445
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2} .0077087{col 58}{space 2} .0064881{col 69}{space 1}    1.19{col 78}{space 3}0.235{col 86}{space 4}-.0050078{col 99}{space 3} .0204253
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .0260556{col 58}{space 2} .0152243{col 69}{space 1}    1.71{col 78}{space 3}0.087{col 86}{space 4}-.0037834{col 99}{space 3} .0558947
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2}-.0518574{col 58}{space 2} .0077008{col 69}{space 1}   -6.73{col 78}{space 3}0.000{col 86}{space 4}-.0669507{col 99}{space 3}-.0367641
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2}-.0237974{col 58}{space 2} .0127763{col 69}{space 1}   -1.86{col 78}{space 3}0.063{col 86}{space 4}-.0488385{col 99}{space 3} .0012436
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2}  .021198{col 58}{space 2} .0079724{col 69}{space 1}    2.66{col 78}{space 3}0.008{col 86}{space 4} .0055724{col 99}{space 3} .0368235
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2}-.0036342{col 58}{space 2} .0061523{col 69}{space 1}   -0.59{col 78}{space 3}0.555{col 86}{space 4}-.0156925{col 99}{space 3} .0084242
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2}-.0086136{col 58}{space 2} .0083551{col 69}{space 1}   -1.03{col 78}{space 3}0.303{col 86}{space 4}-.0249893{col 99}{space 3} .0077621
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.0809858{col 58}{space 2} .0069343{col 69}{space 1}  -11.68{col 78}{space 3}0.000{col 86}{space 4}-.0945767{col 99}{space 3}-.0673948
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2} .0333195{col 58}{space 2} .0123759{col 69}{space 1}    2.69{col 78}{space 3}0.007{col 86}{space 4} .0090631{col 99}{space 3} .0575759
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2}-.0029669{col 58}{space 2} .0076754{col 69}{space 1}   -0.39{col 78}{space 3}0.699{col 86}{space 4}-.0180105{col 99}{space 3} .0120768
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0059415{col 58}{space 2} .0037032{col 69}{space 1}    1.60{col 78}{space 3}0.109{col 86}{space 4}-.0013166{col 99}{space 3} .0131997
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0034338{col 58}{space 2} .0035705{col 69}{space 1}    0.96{col 78}{space 3}0.336{col 86}{space 4}-.0035643{col 99}{space 3} .0104318
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0067643{col 58}{space 2} .0039464{col 69}{space 1}    1.71{col 78}{space 3}0.087{col 86}{space 4}-.0009705{col 99}{space 3} .0144991
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0089475{col 58}{space 2} .0036472{col 69}{space 1}    2.45{col 78}{space 3}0.014{col 86}{space 4} .0017991{col 99}{space 3} .0160959
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2} .0002395{col 58}{space 2} .0049196{col 69}{space 1}    0.05{col 78}{space 3}0.961{col 86}{space 4}-.0094028{col 99}{space 3} .0098817
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2} .0103794{col 58}{space 2} .0047349{col 69}{space 1}    2.19{col 78}{space 3}0.028{col 86}{space 4} .0010992{col 99}{space 3} .0196596
{txt}{space 44} {c |}
{space 38}female {c |}{col 46}{res}{space 2}-.0164733{col 58}{space 2} .0015286{col 69}{space 1}  -10.78{col 78}{space 3}0.000{col 86}{space 4}-.0194692{col 99}{space 3}-.0134773
{txt}{space 41}age {c |}{col 46}{res}{space 2} .0016009{col 58}{space 2}  .000073{col 69}{space 1}   21.93{col 78}{space 3}0.000{col 86}{space 4} .0014578{col 99}{space 3}  .001744
{txt}{space 44} {c |}
{space 35}education {c |}
{space 34}Secondary  {c |}{col 46}{res}{space 2}-.0253376{col 58}{space 2} .0020455{col 69}{space 1}  -12.39{col 78}{space 3}0.000{col 86}{space 4}-.0293467{col 99}{space 3}-.0213284
{txt}{space 27}Higher Education  {c |}{col 46}{res}{space 2}-.0383923{col 58}{space 2} .0021903{col 69}{space 1}  -17.53{col 78}{space 3}0.000{col 86}{space 4}-.0426852{col 99}{space 3}-.0340995
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0031692{col 58}{space 2} .0024066{col 69}{space 1}    1.32{col 78}{space 3}0.188{col 86}{space 4}-.0015477{col 99}{space 3}  .007886
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0381288{col 58}{space 2} .0027624{col 69}{space 1}   13.80{col 78}{space 3}0.000{col 86}{space 4} .0327147{col 99}{space 3}  .043543
{txt}{space 36}Student  {c |}{col 46}{res}{space 2}   .01818{col 58}{space 2}  .004267{col 69}{space 1}    4.26{col 78}{space 3}0.000{col 86}{space 4} .0098167{col 99}{space 3} .0265432
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2} .0176063{col 58}{space 2} .0039042{col 69}{space 1}    4.51{col 78}{space 3}0.000{col 86}{space 4} .0099542{col 99}{space 3} .0252585
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2} .0239042{col 58}{space 2} .0060549{col 69}{space 1}    3.95{col 78}{space 3}0.000{col 86}{space 4} .0120368{col 99}{space 3} .0357717
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0177837{col 58}{space 2} .0052009{col 69}{space 1}    3.42{col 78}{space 3}0.001{col 86}{space 4}   .00759{col 99}{space 3} .0279773
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0249115{col 58}{space 2} .0048769{col 69}{space 1}    5.11{col 78}{space 3}0.000{col 86}{space 4}  .015353{col 99}{space 3} .0344701
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0162273{col 58}{space 2} .0052735{col 69}{space 1}    3.08{col 78}{space 3}0.002{col 86}{space 4} .0058914{col 99}{space 3} .0265632
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0221972{col 58}{space 2} .0047693{col 69}{space 1}    4.65{col 78}{space 3}0.000{col 86}{space 4} .0128496{col 99}{space 3} .0315448
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0132552{col 58}{space 2} .0067721{col 69}{space 1}   -1.96{col 78}{space 3}0.050{col 86}{space 4}-.0265283{col 99}{space 3} .0000179
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#female {c |}
{space 40}0 1  {c |}{col 46}{res}{space 2} .0275685{col 58}{space 2} .0022283{col 69}{space 1}   12.37{col 78}{space 3}0.000{col 86}{space 4}  .023201{col 99}{space 3} .0319359
{txt}{space 40}1 0  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 40}1 1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2}-.0012239{col 58}{space 2} .0000916{col 69}{space 1}  -13.36{col 78}{space 3}0.000{col 86}{space 4}-.0014035{col 99}{space 3}-.0010444
{txt}{space 44} {c |}
{space 25}preperiod#education {c |}
{space 19}1#Primary School or less  {c |}{col 46}{res}{space 2} .0142822{col 58}{space 2} .0030823{col 69}{space 1}    4.63{col 78}{space 3}0.000{col 86}{space 4} .0082409{col 99}{space 3} .0203235
{txt}{space 32}1#Secondary  {c |}{col 46}{res}{space 2} .0026042{col 58}{space 2} .0030749{col 69}{space 1}    0.85{col 78}{space 3}0.397{col 86}{space 4}-.0034226{col 99}{space 3}  .008631
{txt}{space 25}1#Higher Education  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2} .0014772{col 58}{space 2} .0044814{col 69}{space 1}    0.33{col 78}{space 3}0.742{col 86}{space 4}-.0073062{col 99}{space 3} .0102605
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2} -.008749{col 58}{space 2} .0050576{col 69}{space 1}   -1.73{col 78}{space 3}0.084{col 86}{space 4}-.0186617{col 99}{space 3} .0011637
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2} .0017754{col 58}{space 2} .0045586{col 69}{space 1}    0.39{col 78}{space 3}0.697{col 86}{space 4}-.0071593{col 99}{space 3} .0107101
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2}-.0494877{col 58}{space 2} .0068284{col 69}{space 1}   -7.25{col 78}{space 3}0.000{col 86}{space 4}-.0628711{col 99}{space 3}-.0361042
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2}-.0047795{col 58}{space 2} .0100734{col 69}{space 1}   -0.47{col 78}{space 3}0.635{col 86}{space 4}-.0245231{col 99}{space 3} .0149641
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2}-.0545075{col 58}{space 2} .0133659{col 69}{space 1}   -4.08{col 78}{space 3}0.000{col 86}{space 4}-.0807042{col 99}{space 3}-.0283108
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .0688532{col 58}{space 2} .0132696{col 69}{space 1}    5.19{col 78}{space 3}0.000{col 86}{space 4} .0428452{col 99}{space 3} .0948611
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0641392{col 58}{space 2} .0097032{col 69}{space 1}    6.61{col 78}{space 3}0.000{col 86}{space 4} .0451213{col 99}{space 3} .0831572
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0566645{col 58}{space 2} .0117468{col 69}{space 1}    4.82{col 78}{space 3}0.000{col 86}{space 4} .0336412{col 99}{space 3} .0796878
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2} .0143908{col 58}{space 2} .0101692{col 69}{space 1}    1.42{col 78}{space 3}0.157{col 86}{space 4}-.0055404{col 99}{space 3} .0343221
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0375457{col 58}{space 2} .0162578{col 69}{space 1}    2.31{col 78}{space 3}0.021{col 86}{space 4} .0056808{col 99}{space 3} .0694105
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0558594{col 58}{space 2} .0113233{col 69}{space 1}    4.93{col 78}{space 3}0.000{col 86}{space 4} .0336661{col 99}{space 3} .0780527
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} .0395648{col 58}{space 2}  .008704{col 69}{space 1}    4.55{col 78}{space 3}0.000{col 86}{space 4} .0225052{col 99}{space 3} .0566245
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0384985{col 58}{space 2} .0132058{col 69}{space 1}   -2.92{col 78}{space 3}0.004{col 86}{space 4}-.0643815{col 99}{space 3}-.0126155
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2} .0461781{col 58}{space 2} .0126988{col 69}{space 1}    3.64{col 78}{space 3}0.000{col 86}{space 4} .0212889{col 99}{space 3} .0710673
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .0811614{col 58}{space 2} .0102561{col 69}{space 1}    7.91{col 78}{space 3}0.000{col 86}{space 4} .0610598{col 99}{space 3} .1012631
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .0496903{col 58}{space 2} .0112757{col 69}{space 1}    4.41{col 78}{space 3}0.000{col 86}{space 4} .0275904{col 99}{space 3} .0717902
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .0915812{col 58}{space 2} .0120069{col 69}{space 1}    7.63{col 78}{space 3}0.000{col 86}{space 4} .0680481{col 99}{space 3} .1151142
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2} .0758966{col 58}{space 2} .0124964{col 69}{space 1}    6.07{col 78}{space 3}0.000{col 86}{space 4}  .051404{col 99}{space 3} .1003892
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2} -.002827{col 58}{space 2}  .011215{col 69}{space 1}   -0.25{col 78}{space 3}0.801{col 86}{space 4} -.024808{col 99}{space 3}  .019154
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2} .0432178{col 58}{space 2} .0150156{col 69}{space 1}    2.88{col 78}{space 3}0.004{col 86}{space 4} .0137877{col 99}{space 3} .0726478
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0802113{col 58}{space 2} .0110179{col 69}{space 1}   -7.28{col 78}{space 3}0.000{col 86}{space 4} -.101806{col 99}{space 3}-.0586166
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2} .0127723{col 58}{space 2} .0114889{col 69}{space 1}    1.11{col 78}{space 3}0.266{col 86}{space 4}-.0097456{col 99}{space 3} .0352903
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2} .0369953{col 58}{space 2} .0107393{col 69}{space 1}    3.44{col 78}{space 3}0.001{col 86}{space 4} .0159467{col 99}{space 3} .0580439
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2}  .003066{col 58}{space 2} .0154463{col 69}{space 1}    0.20{col 78}{space 3}0.843{col 86}{space 4}-.0272084{col 99}{space 3} .0333403
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} .0597484{col 58}{space 2} .0123776{col 69}{space 1}    4.83{col 78}{space 3}0.000{col 86}{space 4} .0354888{col 99}{space 3} .0840081
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2}-.0031054{col 58}{space 2} .0151466{col 69}{space 1}   -0.21{col 78}{space 3}0.838{col 86}{space 4}-.0327922{col 99}{space 3} .0265815
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .0468831{col 58}{space 2} .0104427{col 69}{space 1}    4.49{col 78}{space 3}0.000{col 86}{space 4} .0264157{col 99}{space 3} .0673506
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2} .0285512{col 58}{space 2} .0113715{col 69}{space 1}    2.51{col 78}{space 3}0.012{col 86}{space 4} .0062633{col 99}{space 3}  .050839
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0384223{col 58}{space 2} .0128159{col 69}{space 1}   -3.00{col 78}{space 3}0.003{col 86}{space 4} -.063541{col 99}{space 3}-.0133036
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .0307852{col 58}{space 2} .0106566{col 69}{space 1}    2.89{col 78}{space 3}0.004{col 86}{space 4} .0098987{col 99}{space 3} .0516718
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2} .0092251{col 58}{space 2}  .012128{col 69}{space 1}    0.76{col 78}{space 3}0.447{col 86}{space 4}-.0145454{col 99}{space 3} .0329956
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2} .0209309{col 58}{space 2} .0126662{col 69}{space 1}    1.65{col 78}{space 3}0.098{col 86}{space 4}-.0038945{col 99}{space 3} .0457563
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0011353{col 58}{space 2} .0131096{col 69}{space 1}    0.09{col 78}{space 3}0.931{col 86}{space 4}-.0245592{col 99}{space 3} .0268298
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0173812{col 58}{space 2} .0088706{col 69}{space 1}    1.96{col 78}{space 3}0.050{col 86}{space 4}-4.84e-06{col 99}{space 3} .0347672
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .0825242{col 58}{space 2}   .00935{col 69}{space 1}    8.83{col 78}{space 3}0.000{col 86}{space 4} .0641985{col 99}{space 3} .1008499
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2} .0301619{col 58}{space 2} .0098007{col 69}{space 1}    3.08{col 78}{space 3}0.002{col 86}{space 4} .0109527{col 99}{space 3}  .049371
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2}-.0167034{col 58}{space 2} .0112984{col 69}{space 1}   -1.48{col 78}{space 3}0.139{col 86}{space 4}-.0388479{col 99}{space 3} .0054411
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2}-.0366884{col 58}{space 2} .0132517{col 69}{space 1}   -2.77{col 78}{space 3}0.006{col 86}{space 4}-.0626614{col 99}{space 3}-.0107154
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2} .0157385{col 58}{space 2} .0157631{col 69}{space 1}    1.00{col 78}{space 3}0.318{col 86}{space 4}-.0151568{col 99}{space 3} .0466337
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}-.0302929{col 58}{space 2} .0104892{col 69}{space 1}   -2.89{col 78}{space 3}0.004{col 86}{space 4}-.0508514{col 99}{space 3}-.0097345
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .0549467{col 58}{space 2} .0131933{col 69}{space 1}    4.16{col 78}{space 3}0.000{col 86}{space 4} .0290883{col 99}{space 3} .0808052
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0066135{col 58}{space 2} .0108534{col 69}{space 1}   -0.61{col 78}{space 3}0.542{col 86}{space 4}-.0278859{col 99}{space 3} .0146588
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0084924{col 58}{space 2} .0164663{col 69}{space 1}   -0.52{col 78}{space 3}0.606{col 86}{space 4}-.0407658{col 99}{space 3} .0237809
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .0877425{col 58}{space 2} .0089431{col 69}{space 1}    9.81{col 78}{space 3}0.000{col 86}{space 4} .0702143{col 99}{space 3} .1052708
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2} .0253622{col 58}{space 2} .0200428{col 69}{space 1}    1.27{col 78}{space 3}0.206{col 86}{space 4} -.013921{col 99}{space 3} .0646454
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2} .0143275{col 58}{space 2} .0112277{col 69}{space 1}    1.28{col 78}{space 3}0.202{col 86}{space 4}-.0076785{col 99}{space 3} .0363335
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0608624{col 58}{space 2} .0165208{col 69}{space 1}    3.68{col 78}{space 3}0.000{col 86}{space 4} .0284821{col 99}{space 3} .0932427
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2} .0019134{col 58}{space 2}  .011691{col 69}{space 1}    0.16{col 78}{space 3}0.870{col 86}{space 4}-.0210007{col 99}{space 3} .0248274
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2}  .050554{col 58}{space 2} .0086374{col 69}{space 1}    5.85{col 78}{space 3}0.000{col 86}{space 4}  .033625{col 99}{space 3} .0674829
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2} .0347178{col 58}{space 2} .0121427{col 69}{space 1}    2.86{col 78}{space 3}0.004{col 86}{space 4} .0109184{col 99}{space 3} .0585171
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0677139{col 58}{space 2} .0100581{col 69}{space 1}   -6.73{col 78}{space 3}0.000{col 86}{space 4}-.0874274{col 99}{space 3}-.0480004
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2} .0025222{col 58}{space 2} .0161795{col 69}{space 1}    0.16{col 78}{space 3}0.876{col 86}{space 4}-.0291892{col 99}{space 3} .0342335
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1741285{col 58}{space 2} .0089942{col 69}{space 1}   19.36{col 78}{space 3}0.000{col 86}{space 4} .1565001{col 99}{space 3} .1917568
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}225{txt}, {res}701799{txt}) = {res}29.059{col 62}{txt} Prob > F = {res}0.000
{txt}
{com}. 
. estadd local SurveyFE "$\checkmark$"

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

{com}. estadd local ProvinceFE "$\checkmark$"

{txt}added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

{com}. estadd local YearFE "$\checkmark$"

{txt}added macro:
             e(YearFE) : "{res:$\checkmark$}"

{com}. estadd local PeriodFE "$\checkmark$"

{txt}added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

{com}. estadd local PeriodCtrols "$\checkmark$"

{txt}added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

{com}. estadd local Data "Ours"

{txt}added macro:
               e(Data) : "{res:Ours}"

{com}. estadd local Sample "Pooled"

{txt}added macro:
             e(Sample) : "{res:Pooled}"

{com}. estadd local Propensity "$\checkmark$"

{txt}added macro:
         e(Propensity) : "{res:$\checkmark$}"

{com}. estadd local Estimation "OLS"

{txt}added macro:
         e(Estimation) : "{res:OLS}"

{com}. estadd local Outcome "All year"

{txt}added macro:
            e(Outcome) : "{res:All year}"

{com}. 
. boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.0536
{col 25}{txt}Prob>|t| = {res}    0.9600

95%{txt} confidence set for null hypothesis expression: {res}[−.0149, .01346]
{txt}
{com}. 
. 
. **** Build Appendix Table 19: 
. esttab a_reppkg a_reppkg_Q1 b_preAll b_postAll b_pooledAll using ${c -(}tables{c )-}Survey_Appendix_Table_19.tex, ///
>                 keep(top_prizes_gdp_ours) nocon r2 nostar ///           
>                 cells(b(fmt(3)) se(fmt(3) par) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>                 mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>                 coeflabels(top_prizes_gdp_ours "Top Lottery prizes" expenditure_gdp_ours "Lottery expenditure") ///
>                 scalars("Estimation Estimation" "Data Data" "Sample Sample" "Outcome Time Outcome" "ProvinceFE Province FE" "YearFE Year FE" "SurveyFE Survey FE" "PeriodFE Period FE" "PeriodCtrols Period*Controls"  "Propensity Propensity corrected")  replace      
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_19.tex"'})

{com}.                 
. /* NOTE: The WCB confidence sets are manually added to the tables from the Stata output 
> generated here.
> */              
. 
. 
. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLE 20 & 21: Effects of Last Year's Lottery Prizes on Survey 
. *** Measures of Incumbent Party Support by GENDER
. **----------------------------------------------------------------------------**
. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. ** Period control:
. gen preperiod=1 if year_original<2010
{txt}(448,934 missing values generated)

{com}. replace preperiod=0 if year_original>=2010
{txt}(448,934 real changes made)

{com}. 
. global individual_characteristics "i.municipality_size age i.education i.status"
{txt}
{com}. *global individual_characteristics "i.municipality_size female age i.education i.status"
. 
. global ind_char_preperiod "preperiod#i.municipality_size preperiod#c.age preperiod#i.education preperiod#i.status"
{txt}
{com}. *global ind_char_preperiod "preperiod#i.municipality_size preperiod#female preperiod#c.age preperiod#i.education preperiod#i.status"
. 
. 
. * Loop over female==0 (male) and female==1 (female)
. 
. foreach gender in 0 1 {c -(}
{txt}  2{com}.     
.     * Local label depending on value of female
.     if `gender'==0 local genderlabel male
{txt}  3{com}.     else local genderlabel female
{txt}  4{com}.     
.     * Restrict sample
.     preserve
{txt}  5{com}.         keep if month<4 & female==`gender'
{txt}  6{com}.         
.         ** Col 4, Appendix Tables 20, 21: 
.                 eststo b_preQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num $individual_characteristics if year_original<2010, absorb(survey) 
{txt}  7{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt}  8{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt}  9{com}.                 estadd local YearFE "$\checkmark$"
{txt} 10{com}.                 estadd local PeriodFE "$\times$"
{txt} 11{com}.                 estadd local PeriodCtrols "$\times$"
{txt} 12{com}.                 estadd local Data "Ours"
{txt} 13{com}.                 estadd local Sample "Pre-10"
{txt} 14{com}.                 estadd local Propensity "$\checkmark$"
{txt} 15{com}.                 estadd local Estimation "OLS"
{txt} 16{com}.                 estadd local Outcome "Q1"
{txt} 17{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 18{com}. 
. 
.                 ** Col 5, Appendix Tables 20, 21:
.                 eststo b_postQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num $individual_characteristics if year_original>=2010, absorb(survey) 
{txt} 19{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 20{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 21{com}.                 estadd local YearFE "$\checkmark$"
{txt} 22{com}.                 estadd local PeriodFE "$\times$"
{txt} 23{com}.                 estadd local PeriodCtrols "$\times$"
{txt} 24{com}.                 estadd local Data "Ours"
{txt} 25{com}.                 estadd local Sample "Post-09"
{txt} 26{com}.                 estadd local Propensity "$\checkmark$"
{txt} 27{com}.                 estadd local Estimation "OLS"
{txt} 28{com}.                 estadd local Outcome "Q1"
{txt} 29{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 30{com}. 
. 
.                 ** Col 6, Appendix Tables 20, 21: 
.                 eststo b_pooledQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num preperiod $individual_characteristics $ind_char_preperiod i.prov_num#preperiod, absorb(survey) 
{txt} 31{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 32{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 33{com}.                 estadd local YearFE "$\checkmark$"
{txt} 34{com}.                 estadd local PeriodFE "$\checkmark$"
{txt} 35{com}.                 estadd local PeriodCtrols "$\checkmark$"
{txt} 36{com}.                 estadd local Data "Ours"
{txt} 37{com}.                 estadd local Sample "Pooled"
{txt} 38{com}.                 estadd local Propensity "$\checkmark$"
{txt} 39{com}.                 estadd local Estimation "OLS"
{txt} 40{com}.                 estadd local Outcome "Q1"
{txt} 41{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 42{com}. 
.                 *** Appendix Tables 20, 21: Assemble tables 
. 
.                 esttab b_preQ1 b_postQ1 b_pooledQ1 using ${c -(}tables{c )-}Survey_Appendix_Tables_20_21_`genderlabel'.tex, ///
>                                 keep(top_prizes_gdp_ours) nocon r2 nostar ///           
>                                 cells(b(fmt(3)) se(fmt(3) par) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>                                 mtitles se mtitles("Vote Incumb." "Vote Incumb." ///
>                                 "Vote Incumb.")  ///
>                                 coeflabels(top_prizes_gdp_ours "Top Lottery prizes" expenditure_gdp_ours "Lottery expenditure") ///
>                                 scalars("Estimation Estimation" "Data Data" "Sample Sample" "Outcome Time Outcome" "ProvinceFE Province FE" "YearFE Year FE" "SurveyFE Survey FE" "Propensity Propensity corrected")            replace                         
{txt} 43{com}.                                 
.                                 
.                 /* NOTE: The WCB confidence sets are manually added to the tables from the Stata output generated here. */
. 
.     restore
{txt} 44{com}. {c )-}
{txt}(961,256 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:69,151}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:86}, {res:69030})}{col 70} = {res}{ralign 6:28.53}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0413}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0397}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4401}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0014267{col 56}{space 2} .0060107{col 67}{space 1}    0.24{col 76}{space 3}0.812{col 84}{space 4}-.0103543{col 97}{space 3} .0132076
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0102707{col 56}{space 2} .1756393{col 67}{space 1}    0.06{col 76}{space 3}0.953{col 84}{space 4}-.3339821{col 97}{space 3} .3545235
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}-.0903443{col 56}{space 2} .2447187{col 67}{space 1}   -0.37{col 76}{space 3}0.712{col 84}{space 4}-.5699925{col 97}{space 3}  .389304
{txt}{space 37}1989  {c |}{col 44}{res}{space 2}-.0736529{col 56}{space 2} .2022121{col 67}{space 1}   -0.36{col 76}{space 3}0.716{col 84}{space 4}-.4699883{col 97}{space 3} .3226825
{txt}{space 37}1990  {c |}{col 44}{res}{space 2}-.3139681{col 56}{space 2} .2020304{col 67}{space 1}   -1.55{col 76}{space 3}0.120{col 84}{space 4}-.7099473{col 97}{space 3} .0820111
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.4946386{col 56}{space 2}  .251812{col 67}{space 1}   -1.96{col 76}{space 3}0.049{col 84}{space 4}-.9881898{col 97}{space 3}-.0010874
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.3421952{col 56}{space 2} .2106745{col 67}{space 1}   -1.62{col 76}{space 3}0.104{col 84}{space 4}-.7551169{col 97}{space 3} .0707265
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}-.0982901{col 56}{space 2} .1971709{col 67}{space 1}   -0.50{col 76}{space 3}0.618{col 84}{space 4}-.4847448{col 97}{space 3} .2881645
{txt}{space 37}1994  {c |}{col 44}{res}{space 2}-.0438777{col 56}{space 2} .1997301{col 67}{space 1}   -0.22{col 76}{space 3}0.826{col 84}{space 4}-.4353482{col 97}{space 3} .3475929
{txt}{space 37}1995  {c |}{col 44}{res}{space 2}-.0735591{col 56}{space 2} .1956764{col 67}{space 1}   -0.38{col 76}{space 3}0.707{col 84}{space 4}-.4570846{col 97}{space 3} .3099664
{txt}{space 37}1996  {c |}{col 44}{res}{space 2}-.0257591{col 56}{space 2} .2130933{col 67}{space 1}   -0.12{col 76}{space 3}0.904{col 84}{space 4}-.4434216{col 97}{space 3} .3919034
{txt}{space 37}1997  {c |}{col 44}{res}{space 2} .2684926{col 56}{space 2}  .240123{col 67}{space 1}    1.12{col 76}{space 3}0.264{col 84}{space 4}-.2021481{col 97}{space 3} .7391333
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .4350561{col 56}{space 2} .2530451{col 67}{space 1}    1.72{col 76}{space 3}0.086{col 84}{space 4}-.0609119{col 97}{space 3}  .931024
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .4839732{col 56}{space 2}  .248352{col 67}{space 1}    1.95{col 76}{space 3}0.051{col 84}{space 4}-.0027963{col 97}{space 3} .9707426
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .3946069{col 56}{space 2} .1777771{col 67}{space 1}    2.22{col 76}{space 3}0.026{col 84}{space 4} .0461641{col 97}{space 3} .7430497
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} .3155874{col 56}{space 2} .2375147{col 67}{space 1}    1.33{col 76}{space 3}0.184{col 84}{space 4} -.149941{col 97}{space 3} .7811158
{txt}{space 37}2002  {c |}{col 44}{res}{space 2}  .503198{col 56}{space 2} .2425114{col 67}{space 1}    2.07{col 76}{space 3}0.038{col 84}{space 4}  .027876{col 97}{space 3}   .97852
{txt}{space 37}2003  {c |}{col 44}{res}{space 2}-.1062578{col 56}{space 2} .2324149{col 67}{space 1}   -0.46{col 76}{space 3}0.648{col 84}{space 4}-.5617907{col 97}{space 3} .3492751
{txt}{space 37}2004  {c |}{col 44}{res}{space 2} .3588166{col 56}{space 2} .1766387{col 67}{space 1}    2.03{col 76}{space 3}0.042{col 84}{space 4} .0126051{col 97}{space 3} .7050282
{txt}{space 37}2005  {c |}{col 44}{res}{space 2}-.1003301{col 56}{space 2} .2307522{col 67}{space 1}   -0.43{col 76}{space 3}0.664{col 84}{space 4} -.552604{col 97}{space 3} .3519437
{txt}{space 37}2006  {c |}{col 44}{res}{space 2}-.2059112{col 56}{space 2} .2171176{col 67}{space 1}   -0.95{col 76}{space 3}0.343{col 84}{space 4}-.6314613{col 97}{space 3} .2196389
{txt}{space 37}2007  {c |}{col 44}{res}{space 2}   .06041{col 56}{space 2}  .208422{col 67}{space 1}    0.29{col 76}{space 3}0.772{col 84}{space 4}-.3480967{col 97}{space 3} .4689167
{txt}{space 37}2008  {c |}{col 44}{res}{space 2} .0407419{col 56}{space 2} .1757187{col 67}{space 1}    0.23{col 76}{space 3}0.817{col 84}{space 4}-.3036664{col 97}{space 3} .3851501
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}-.3490624{col 56}{space 2} .2156114{col 67}{space 1}   -1.62{col 76}{space 3}0.105{col 84}{space 4}-.7716604{col 97}{space 3} .0735357
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1054566{col 56}{space 2}  .018871{col 67}{space 1}   -5.59{col 76}{space 3}0.000{col 84}{space 4}-.1424439{col 97}{space 3}-.0684694
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0653352{col 56}{space 2} .0184261{col 67}{space 1}    3.55{col 76}{space 3}0.000{col 84}{space 4}   .02922{col 97}{space 3} .1014504
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2}  .088786{col 56}{space 2} .0147871{col 67}{space 1}    6.00{col 76}{space 3}0.000{col 84}{space 4} .0598033{col 97}{space 3} .1177686
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0655523{col 56}{space 2} .0176231{col 67}{space 1}    3.72{col 76}{space 3}0.000{col 84}{space 4}  .031011{col 97}{space 3} .1000936
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0202988{col 56}{space 2}  .014882{col 67}{space 1}    1.36{col 76}{space 3}0.173{col 84}{space 4}-.0088699{col 97}{space 3} .0494674
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0605545{col 56}{space 2} .0219324{col 67}{space 1}    2.76{col 76}{space 3}0.006{col 84}{space 4} .0175671{col 97}{space 3} .1035419
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0991682{col 56}{space 2} .0173515{col 67}{space 1}    5.72{col 76}{space 3}0.000{col 84}{space 4} .0651592{col 97}{space 3} .1331772
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0213531{col 56}{space 2}  .012487{col 67}{space 1}   -1.71{col 76}{space 3}0.087{col 84}{space 4}-.0458277{col 97}{space 3} .0031215
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} -.021248{col 56}{space 2} .0185528{col 67}{space 1}   -1.15{col 76}{space 3}0.252{col 84}{space 4}-.0576115{col 97}{space 3} .0151154
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0972366{col 56}{space 2} .0178097{col 67}{space 1}    5.46{col 76}{space 3}0.000{col 84}{space 4} .0623296{col 97}{space 3} .1321437
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0985588{col 56}{space 2} .0170652{col 67}{space 1}    5.78{col 76}{space 3}0.000{col 84}{space 4} .0651109{col 97}{space 3} .1320066
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0656539{col 56}{space 2} .0171567{col 67}{space 1}    3.83{col 76}{space 3}0.000{col 84}{space 4} .0320268{col 97}{space 3}  .099281
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0900283{col 56}{space 2} .0173274{col 67}{space 1}    5.20{col 76}{space 3}0.000{col 84}{space 4} .0560667{col 97}{space 3}   .12399
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0660133{col 56}{space 2} .0181503{col 67}{space 1}    3.64{col 76}{space 3}0.000{col 84}{space 4} .0304387{col 97}{space 3} .1015879
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0359626{col 56}{space 2} .0158962{col 67}{space 1}   -2.26{col 76}{space 3}0.024{col 84}{space 4}-.0671191{col 97}{space 3}-.0048061
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0584803{col 56}{space 2} .0210988{col 67}{space 1}    2.77{col 76}{space 3}0.006{col 84}{space 4} .0171266{col 97}{space 3} .0998339
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1711419{col 56}{space 2} .0158793{col 67}{space 1}  -10.78{col 76}{space 3}0.000{col 84}{space 4}-.2022652{col 97}{space 3}-.1400186
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0800423{col 56}{space 2} .0179949{col 67}{space 1}   -4.45{col 76}{space 3}0.000{col 84}{space 4}-.1153123{col 97}{space 3}-.0447724
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0309886{col 56}{space 2} .0161887{col 67}{space 1}    1.91{col 76}{space 3}0.056{col 84}{space 4}-.0007412{col 97}{space 3} .0627185
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0816108{col 56}{space 2}  .021005{col 67}{space 1}    3.89{col 76}{space 3}0.000{col 84}{space 4}  .040441{col 97}{space 3} .1227805
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0635174{col 56}{space 2} .0199945{col 67}{space 1}    3.18{col 76}{space 3}0.001{col 84}{space 4} .0243282{col 97}{space 3} .1027066
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0057076{col 56}{space 2} .0202205{col 67}{space 1}   -0.28{col 76}{space 3}0.778{col 84}{space 4}-.0453397{col 97}{space 3} .0339245
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0546796{col 56}{space 2} .0173238{col 67}{space 1}    3.16{col 76}{space 3}0.002{col 84}{space 4} .0207249{col 97}{space 3} .0886343
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0378498{col 56}{space 2} .0163918{col 67}{space 1}    2.31{col 76}{space 3}0.021{col 84}{space 4} .0057219{col 97}{space 3} .0699778
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2}-.0088827{col 56}{space 2} .0195327{col 67}{space 1}   -0.45{col 76}{space 3}0.649{col 84}{space 4}-.0471668{col 97}{space 3} .0294013
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0907809{col 56}{space 2} .0171035{col 67}{space 1}    5.31{col 76}{space 3}0.000{col 84}{space 4} .0572581{col 97}{space 3} .1243036
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0225596{col 56}{space 2} .0174848{col 67}{space 1}    1.29{col 76}{space 3}0.197{col 84}{space 4}-.0117105{col 97}{space 3} .0568297
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.1122601{col 56}{space 2}   .02046{col 67}{space 1}   -5.49{col 76}{space 3}0.000{col 84}{space 4}-.1523616{col 97}{space 3}-.0721585
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0544882{col 56}{space 2} .0181843{col 67}{space 1}    3.00{col 76}{space 3}0.003{col 84}{space 4}  .018847{col 97}{space 3} .0901293
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2}  .007972{col 56}{space 2} .0130906{col 67}{space 1}    0.61{col 76}{space 3}0.543{col 84}{space 4}-.0176855{col 97}{space 3} .0336295
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .1028512{col 56}{space 2} .0153819{col 67}{space 1}    6.69{col 76}{space 3}0.000{col 84}{space 4} .0727028{col 97}{space 3} .1329996
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2}  .052256{col 56}{space 2} .0150636{col 67}{space 1}    3.47{col 76}{space 3}0.001{col 84}{space 4} .0227313{col 97}{space 3} .0817807
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0408821{col 56}{space 2} .0175832{col 67}{space 1}   -2.33{col 76}{space 3}0.020{col 84}{space 4}-.0753451{col 97}{space 3}-.0064191
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0365146{col 56}{space 2} .0182421{col 67}{space 1}    2.00{col 76}{space 3}0.045{col 84}{space 4} .0007601{col 97}{space 3} .0722692
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0436464{col 56}{space 2} .0223469{col 67}{space 1}    1.95{col 76}{space 3}0.051{col 84}{space 4}-.0001536{col 97}{space 3} .0874464
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0082543{col 56}{space 2} .0154413{col 67}{space 1}   -0.53{col 76}{space 3}0.593{col 84}{space 4}-.0385192{col 97}{space 3} .0220105
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0910367{col 56}{space 2} .0183133{col 67}{space 1}    4.97{col 76}{space 3}0.000{col 84}{space 4} .0551426{col 97}{space 3} .1269308
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0447825{col 56}{space 2} .0161932{col 67}{space 1}    2.77{col 76}{space 3}0.006{col 84}{space 4} .0130439{col 97}{space 3} .0765211
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2}-.0203072{col 56}{space 2}  .022431{col 67}{space 1}   -0.91{col 76}{space 3}0.365{col 84}{space 4}-.0642719{col 97}{space 3} .0236574
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2}  .055989{col 56}{space 2} .0153183{col 67}{space 1}    3.66{col 76}{space 3}0.000{col 84}{space 4} .0259652{col 97}{space 3} .0860129
{txt}{space 36}soria  {c |}{col 44}{res}{space 2}  .052103{col 56}{space 2} .0272047{col 67}{space 1}    1.92{col 76}{space 3}0.055{col 84}{space 4}-.0012183{col 97}{space 3} .1054242
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0064256{col 56}{space 2} .0175597{col 67}{space 1}   -0.37{col 76}{space 3}0.714{col 84}{space 4}-.0408425{col 97}{space 3} .0279914
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0393961{col 56}{space 2} .0198486{col 67}{space 1}    1.98{col 76}{space 3}0.047{col 84}{space 4} .0004928{col 97}{space 3} .0782995
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0531753{col 56}{space 2} .0178718{col 67}{space 1}    2.98{col 76}{space 3}0.003{col 84}{space 4} .0181467{col 97}{space 3}  .088204
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0469189{col 56}{space 2} .0141279{col 67}{space 1}    3.32{col 76}{space 3}0.001{col 84}{space 4} .0192282{col 97}{space 3} .0746095
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0254997{col 56}{space 2} .0180646{col 67}{space 1}    1.41{col 76}{space 3}0.158{col 84}{space 4}-.0099069{col 97}{space 3} .0609063
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1731889{col 56}{space 2} .0143938{col 67}{space 1}  -12.03{col 76}{space 3}0.000{col 84}{space 4}-.2014007{col 97}{space 3}-.1449771
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0891347{col 56}{space 2} .0211404{col 67}{space 1}    4.22{col 76}{space 3}0.000{col 84}{space 4} .0476995{col 97}{space 3} .1305699
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0073982{col 56}{space 2}  .017214{col 67}{space 1}    0.43{col 76}{space 3}0.667{col 84}{space 4}-.0263413{col 97}{space 3} .0411376
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0025536{col 56}{space 2} .0068567{col 67}{space 1}   -0.37{col 76}{space 3}0.710{col 84}{space 4}-.0159927{col 97}{space 3} .0108854
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} -.009273{col 56}{space 2} .0068094{col 67}{space 1}   -1.36{col 76}{space 3}0.173{col 84}{space 4}-.0226193{col 97}{space 3} .0040734
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0300585{col 56}{space 2} .0080563{col 67}{space 1}   -3.73{col 76}{space 3}0.000{col 84}{space 4}-.0458487{col 97}{space 3}-.0142682
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0080708{col 56}{space 2} .0070175{col 67}{space 1}   -1.15{col 76}{space 3}0.250{col 84}{space 4}-.0218251{col 97}{space 3} .0056835
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} -.022251{col 56}{space 2} .0109145{col 67}{space 1}   -2.04{col 76}{space 3}0.041{col 84}{space 4}-.0436433{col 97}{space 3}-.0008586
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0145483{col 56}{space 2} .0102504{col 67}{space 1}   -1.42{col 76}{space 3}0.156{col 84}{space 4}-.0346391{col 97}{space 3} .0055425
{txt}{space 42} {c |}
{space 39}age {c |}{col 44}{res}{space 2} .0012244{col 56}{space 2}  .000152{col 67}{space 1}    8.05{col 76}{space 3}0.000{col 84}{space 4} .0009264{col 97}{space 3} .0015224
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.028468{col 56}{space 2} .0043689{col 67}{space 1}   -6.52{col 76}{space 3}0.000{col 84}{space 4} -.037031{col 97}{space 3} -.019905
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0276264{col 56}{space 2} .0051847{col 67}{space 1}   -5.33{col 76}{space 3}0.000{col 84}{space 4}-.0377885{col 97}{space 3}-.0174643
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0194253{col 56}{space 2} .0060085{col 67}{space 1}   -3.23{col 76}{space 3}0.001{col 84}{space 4}-.0312019{col 97}{space 3}-.0076487
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0213412{col 56}{space 2} .0059443{col 67}{space 1}    3.59{col 76}{space 3}0.000{col 84}{space 4} .0096903{col 97}{space 3}  .032992
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0137466{col 56}{space 2} .0071578{col 67}{space 1}   -1.92{col 76}{space 3}0.055{col 84}{space 4}-.0277759{col 97}{space 3} .0002827
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0164313{col 56}{space 2} .0284922{col 67}{space 1}   -0.58{col 76}{space 3}0.564{col 84}{space 4}-.0722761{col 97}{space 3} .0394134
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1940241{col 56}{space 2} .0200537{col 67}{space 1}    9.68{col 76}{space 3}0.000{col 84}{space 4} .1547189{col 97}{space 3} .2333293
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}69030{txt}) = {res}5.532{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.1639
{col 25}{txt}Prob>|t| = {res}    0.8679

95%{txt} confidence set for null hypothesis expression: {res}[−.02099, .02435]
{txt}{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:36,415}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:76}, {res:36318})}{col 70} = {res}{ralign 6:14.80}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0377}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0352}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.3917}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0023147{col 56}{space 2}  .007033{col 67}{space 1}    0.33{col 76}{space 3}0.742{col 84}{space 4}-.0114701{col 97}{space 3} .0160995
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.3412947{col 56}{space 2} .1651452{col 67}{space 1}   -2.07{col 76}{space 3}0.039{col 84}{space 4}-.6649842{col 97}{space 3}-.0176053
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2} .0924488{col 56}{space 2} .1992728{col 67}{space 1}    0.46{col 76}{space 3}0.643{col 84}{space 4}-.2981318{col 97}{space 3} .4830294
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .9385861{col 56}{space 2}  .199172{col 67}{space 1}    4.71{col 76}{space 3}0.000{col 84}{space 4} .5482031{col 97}{space 3} 1.328969
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .5516826{col 56}{space 2} .1890407{col 67}{space 1}    2.92{col 76}{space 3}0.004{col 84}{space 4} .1811573{col 97}{space 3}  .922208
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .5444551{col 56}{space 2} .2058296{col 67}{space 1}    2.65{col 76}{space 3}0.008{col 84}{space 4} .1410231{col 97}{space 3} .9478871
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} .4926178{col 56}{space 2} .2075056{col 67}{space 1}    2.37{col 76}{space 3}0.018{col 84}{space 4} .0859007{col 97}{space 3} .8993349
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .4172015{col 56}{space 2} .1886484{col 67}{space 1}    2.21{col 76}{space 3}0.027{col 84}{space 4} .0474451{col 97}{space 3} .7869579
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .4540235{col 56}{space 2} .1919482{col 67}{space 1}    2.37{col 76}{space 3}0.018{col 84}{space 4} .0777994{col 97}{space 3} .8302477
{txt}{space 37}2018  {c |}{col 44}{res}{space 2} .3504819{col 56}{space 2} .1937749{col 67}{space 1}    1.81{col 76}{space 3}0.071{col 84}{space 4}-.0293226{col 97}{space 3} .7302865
{txt}{space 37}2019  {c |}{col 44}{res}{space 2} .1301995{col 56}{space 2} .1422049{col 67}{space 1}    0.92{col 76}{space 3}0.360{col 84}{space 4}-.1485263{col 97}{space 3} .4089253
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .2086507{col 56}{space 2} .1510089{col 67}{space 1}    1.38{col 76}{space 3}0.167{col 84}{space 4}-.0873312{col 97}{space 3} .5046326
{txt}{space 37}2021  {c |}{col 44}{res}{space 2} .0942529{col 56}{space 2} .1574434{col 67}{space 1}    0.60{col 76}{space 3}0.549{col 84}{space 4}-.2143408{col 97}{space 3} .4028467
{txt}{space 37}2022  {c |}{col 44}{res}{space 2} .0331374{col 56}{space 2} .1458663{col 67}{space 1}    0.23{col 76}{space 3}0.820{col 84}{space 4}-.2527648{col 97}{space 3} .3190395
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.0704672{col 56}{space 2}  .030456{col 67}{space 1}   -2.31{col 76}{space 3}0.021{col 84}{space 4}-.1301617{col 97}{space 3}-.0107726
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0098095{col 56}{space 2} .0259473{col 67}{space 1}    0.38{col 76}{space 3}0.705{col 84}{space 4} -.041048{col 97}{space 3}  .060667
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0614924{col 56}{space 2} .0182022{col 67}{space 1}    3.38{col 76}{space 3}0.001{col 84}{space 4} .0258156{col 97}{space 3} .0971692
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0384286{col 56}{space 2} .0211461{col 67}{space 1}    1.82{col 76}{space 3}0.069{col 84}{space 4}-.0030185{col 97}{space 3} .0798756
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0031828{col 56}{space 2} .0223159{col 67}{space 1}    0.14{col 76}{space 3}0.887{col 84}{space 4} -.040557{col 97}{space 3} .0469226
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}-.0145523{col 56}{space 2} .0340549{col 67}{space 1}   -0.43{col 76}{space 3}0.669{col 84}{space 4} -.081301{col 97}{space 3} .0521964
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0190057{col 56}{space 2} .0213594{col 67}{space 1}    0.89{col 76}{space 3}0.374{col 84}{space 4}-.0228594{col 97}{space 3} .0608707
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0299185{col 56}{space 2} .0201533{col 67}{space 1}   -1.48{col 76}{space 3}0.138{col 84}{space 4}-.0694196{col 97}{space 3} .0095827
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0350674{col 56}{space 2}  .029962{col 67}{space 1}    1.17{col 76}{space 3}0.242{col 84}{space 4}-.0236589{col 97}{space 3} .0937938
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .1096929{col 56}{space 2} .0260485{col 67}{space 1}    4.21{col 76}{space 3}0.000{col 84}{space 4} .0586371{col 97}{space 3} .1607488
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0189863{col 56}{space 2} .0201341{col 67}{space 1}    0.94{col 76}{space 3}0.346{col 84}{space 4}-.0204771{col 97}{space 3} .0584497
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2}-.0066816{col 56}{space 2} .0204828{col 67}{space 1}   -0.33{col 76}{space 3}0.744{col 84}{space 4}-.0468285{col 97}{space 3} .0334654
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0232059{col 56}{space 2} .0220606{col 67}{space 1}    1.05{col 76}{space 3}0.293{col 84}{space 4}-.0200335{col 97}{space 3} .0664454
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0313279{col 56}{space 2} .0258564{col 67}{space 1}    1.21{col 76}{space 3}0.226{col 84}{space 4}-.0193514{col 97}{space 3} .0820072
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}   .01102{col 56}{space 2} .0205674{col 67}{space 1}    0.54{col 76}{space 3}0.592{col 84}{space 4}-.0292927{col 97}{space 3} .0513328
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2}-.0034481{col 56}{space 2} .0335093{col 67}{space 1}   -0.10{col 76}{space 3}0.918{col 84}{space 4}-.0691273{col 97}{space 3} .0622311
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1201971{col 56}{space 2} .0242386{col 67}{space 1}   -4.96{col 76}{space 3}0.000{col 84}{space 4}-.1677054{col 97}{space 3}-.0726888
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0929921{col 56}{space 2} .0265233{col 67}{space 1}   -3.51{col 76}{space 3}0.000{col 84}{space 4}-.1449785{col 97}{space 3}-.0410056
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0508968{col 56}{space 2} .0207114{col 67}{space 1}    2.46{col 76}{space 3}0.014{col 84}{space 4} .0103018{col 97}{space 3} .0914918
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0485712{col 56}{space 2} .0268931{col 67}{space 1}    1.81{col 76}{space 3}0.071{col 84}{space 4}-.0041401{col 97}{space 3} .1012824
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0141122{col 56}{space 2} .0273816{col 67}{space 1}    0.52{col 76}{space 3}0.606{col 84}{space 4}-.0395566{col 97}{space 3} .0677809
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0002127{col 56}{space 2} .0322016{col 67}{space 1}   -0.01{col 76}{space 3}0.995{col 84}{space 4}-.0633288{col 97}{space 3} .0629035
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}-.0108352{col 56}{space 2} .0254274{col 67}{space 1}   -0.43{col 76}{space 3}0.670{col 84}{space 4}-.0606737{col 97}{space 3} .0390032
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0539609{col 56}{space 2} .0217866{col 67}{space 1}    2.48{col 76}{space 3}0.013{col 84}{space 4} .0112586{col 97}{space 3} .0966633
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0532184{col 56}{space 2} .0240483{col 67}{space 1}    2.21{col 76}{space 3}0.027{col 84}{space 4} .0060831{col 97}{space 3} .1003538
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2}  .022382{col 56}{space 2} .0234368{col 67}{space 1}    0.95{col 76}{space 3}0.340{col 84}{space 4}-.0235548{col 97}{space 3} .0683188
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0749752{col 56}{space 2} .0262069{col 67}{space 1}    2.86{col 76}{space 3}0.004{col 84}{space 4} .0236089{col 97}{space 3} .1263414
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0421613{col 56}{space 2} .0258679{col 67}{space 1}   -1.63{col 76}{space 3}0.103{col 84}{space 4}-.0928632{col 97}{space 3} .0085406
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0056561{col 56}{space 2} .0255196{col 67}{space 1}    0.22{col 76}{space 3}0.825{col 84}{space 4} -.044363{col 97}{space 3} .0556752
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0182309{col 56}{space 2} .0174732{col 67}{space 1}    1.04{col 76}{space 3}0.297{col 84}{space 4}-.0160172{col 97}{space 3} .0524789
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0463085{col 56}{space 2} .0181827{col 67}{space 1}    2.55{col 76}{space 3}0.011{col 84}{space 4} .0106699{col 97}{space 3} .0819471
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0341427{col 56}{space 2} .0181674{col 67}{space 1}    1.88{col 76}{space 3}0.060{col 84}{space 4}-.0014659{col 97}{space 3} .0697513
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2} -.028374{col 56}{space 2} .0240649{col 67}{space 1}   -1.18{col 76}{space 3}0.238{col 84}{space 4}-.0755419{col 97}{space 3} .0187938
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0282113{col 56}{space 2}   .02722{col 67}{space 1}    1.04{col 76}{space 3}0.300{col 84}{space 4}-.0251406{col 97}{space 3} .0815632
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0440463{col 56}{space 2}  .033438{col 67}{space 1}    1.32{col 76}{space 3}0.188{col 84}{space 4}-.0214932{col 97}{space 3} .1095858
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} .0217324{col 56}{space 2} .0199042{col 67}{space 1}    1.09{col 76}{space 3}0.275{col 84}{space 4}-.0172805{col 97}{space 3} .0607453
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2}  .018058{col 56}{space 2}  .026976{col 67}{space 1}    0.67{col 76}{space 3}0.503{col 84}{space 4}-.0348157{col 97}{space 3} .0709318
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0658243{col 56}{space 2} .0200387{col 67}{space 1}    3.28{col 76}{space 3}0.001{col 84}{space 4} .0265478{col 97}{space 3} .1051008
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0605292{col 56}{space 2} .0461935{col 67}{space 1}    1.31{col 76}{space 3}0.190{col 84}{space 4}-.0300113{col 97}{space 3} .1510698
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0235449{col 56}{space 2} .0198975{col 67}{space 1}    1.18{col 76}{space 3}0.237{col 84}{space 4}-.0154547{col 97}{space 3} .0625445
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0926812{col 56}{space 2} .0739481{col 67}{space 1}    1.25{col 76}{space 3}0.210{col 84}{space 4}-.0522593{col 97}{space 3} .2376217
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0401183{col 56}{space 2} .0259873{col 67}{space 1}   -1.54{col 76}{space 3}0.123{col 84}{space 4}-.0910542{col 97}{space 3} .0108175
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0343905{col 56}{space 2} .0357618{col 67}{space 1}    0.96{col 76}{space 3}0.336{col 84}{space 4}-.0357036{col 97}{space 3} .1044847
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0378801{col 56}{space 2} .0213608{col 67}{space 1}    1.77{col 76}{space 3}0.076{col 84}{space 4}-.0039877{col 97}{space 3} .0797479
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0307501{col 56}{space 2} .0167101{col 67}{space 1}    1.84{col 76}{space 3}0.066{col 84}{space 4}-.0020022{col 97}{space 3} .0635024
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0177379{col 56}{space 2} .0215881{col 67}{space 1}    0.82{col 76}{space 3}0.411{col 84}{space 4}-.0245753{col 97}{space 3} .0600512
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2} -.079571{col 56}{space 2} .0187888{col 67}{space 1}   -4.24{col 76}{space 3}0.000{col 84}{space 4}-.1163976{col 97}{space 3}-.0427443
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .1023748{col 56}{space 2} .0360326{col 67}{space 1}    2.84{col 76}{space 3}0.004{col 84}{space 4} .0317499{col 97}{space 3} .1729997
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0254947{col 56}{space 2} .0205316{col 67}{space 1}    1.24{col 76}{space 3}0.214{col 84}{space 4}-.0147479{col 97}{space 3} .0657372
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0106555{col 56}{space 2} .0092955{col 67}{space 1}    1.15{col 76}{space 3}0.252{col 84}{space 4} -.007564{col 97}{space 3}  .028875
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0055485{col 56}{space 2} .0089647{col 67}{space 1}    0.62{col 76}{space 3}0.536{col 84}{space 4}-.0120226{col 97}{space 3} .0231196
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0049592{col 56}{space 2} .0100361{col 67}{space 1}    0.49{col 76}{space 3}0.621{col 84}{space 4}-.0147119{col 97}{space 3} .0246302
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0117543{col 56}{space 2} .0092051{col 67}{space 1}    1.28{col 76}{space 3}0.202{col 84}{space 4}-.0062879{col 97}{space 3} .0297965
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0017307{col 56}{space 2} .0127324{col 67}{space 1}    0.14{col 76}{space 3}0.892{col 84}{space 4}-.0232252{col 97}{space 3} .0266865
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0092214{col 56}{space 2} .0124045{col 67}{space 1}    0.74{col 76}{space 3}0.457{col 84}{space 4}-.0150917{col 97}{space 3} .0335346
{txt}{space 42} {c |}
{space 39}age {c |}{col 44}{res}{space 2} .0019426{col 56}{space 2} .0001948{col 67}{space 1}    9.97{col 76}{space 3}0.000{col 84}{space 4} .0015608{col 97}{space 3} .0023244
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0297907{col 56}{space 2} .0051852{col 67}{space 1}   -5.75{col 76}{space 3}0.000{col 84}{space 4}-.0399538{col 97}{space 3}-.0196276
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0508888{col 56}{space 2} .0056804{col 67}{space 1}   -8.96{col 76}{space 3}0.000{col 84}{space 4}-.0620225{col 97}{space 3}-.0397551
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0090186{col 56}{space 2} .0064028{col 67}{space 1}    1.41{col 76}{space 3}0.159{col 84}{space 4}-.0035311{col 97}{space 3} .0215683
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0432703{col 56}{space 2} .0071225{col 67}{space 1}    6.08{col 76}{space 3}0.000{col 84}{space 4} .0293101{col 97}{space 3} .0572306
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0076248{col 56}{space 2} .0108303{col 67}{space 1}    0.70{col 76}{space 3}0.481{col 84}{space 4}-.0136029{col 97}{space 3} .0288525
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} -.007729{col 56}{space 2} .0629328{col 67}{space 1}   -0.12{col 76}{space 3}0.902{col 84}{space 4} -.131079{col 97}{space 3} .1156211
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1294854{col 56}{space 2} .0378146{col 67}{space 1}    3.42{col 76}{space 3}0.001{col 84}{space 4} .0553677{col 97}{space 3}  .203603
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}36318{txt}) = {res}5.295{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.3329
{col 25}{txt}Prob>|t| = {res}    0.7447

95%{txt} confidence set for null hypothesis expression: {res}[−.01815, .02023]
{txt}{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#3.education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:105,566}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:56}
{txt}{col 52}{lalign 17:F({res:161}, {res:105349})}{col 69} = {res}{ralign 7:22.38}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0480}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0460}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4240}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2} .0017522{col 58}{space 2} .0046093{col 69}{space 1}    0.38{col 78}{space 3}0.704{col 86}{space 4} -.007282{col 99}{space 3} .0107864
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2} .0104215{col 58}{space 2} .1692287{col 69}{space 1}    0.06{col 78}{space 3}0.951{col 86}{space 4}-.3212644{col 99}{space 3} .3421074
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}-.0905325{col 58}{space 2} .2357889{col 69}{space 1}   -0.38{col 78}{space 3}0.701{col 86}{space 4}-.5526754{col 99}{space 3} .3716105
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.0737502{col 58}{space 2} .1948377{col 69}{space 1}   -0.38{col 78}{space 3}0.705{col 86}{space 4}-.4556295{col 99}{space 3} .3081291
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.3141059{col 58}{space 2} .1946598{col 69}{space 1}   -1.61{col 78}{space 3}0.107{col 86}{space 4}-.6956364{col 99}{space 3} .0674247
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}  -.49485{col 58}{space 2} .2426216{col 69}{space 1}   -2.04{col 78}{space 3}0.041{col 86}{space 4}-.9703851{col 99}{space 3}-.0193148
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}-.3423733{col 58}{space 2} .2029854{col 69}{space 1}   -1.69{col 78}{space 3}0.092{col 86}{space 4}-.7402219{col 99}{space 3} .0554754
{txt}{space 39}1993  {c |}{col 46}{res}{space 2}-.0981959{col 58}{space 2} .1899804{col 69}{space 1}   -0.52{col 78}{space 3}0.605{col 86}{space 4}-.4705549{col 99}{space 3} .2741631
{txt}{space 39}1994  {c |}{col 46}{res}{space 2}-.0438656{col 58}{space 2} .1924489{col 69}{space 1}   -0.23{col 78}{space 3}0.820{col 86}{space 4}-.4210629{col 99}{space 3} .3333317
{txt}{space 39}1995  {c |}{col 46}{res}{space 2}-.0736086{col 58}{space 2} .1885424{col 69}{space 1}   -0.39{col 78}{space 3}0.696{col 86}{space 4}-.4431491{col 99}{space 3}  .295932
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}-.0253629{col 58}{space 2} .2052807{col 69}{space 1}   -0.12{col 78}{space 3}0.902{col 86}{space 4}-.4277103{col 99}{space 3} .3769845
{txt}{space 39}1997  {c |}{col 46}{res}{space 2} .2681213{col 58}{space 2} .2313349{col 69}{space 1}    1.16{col 78}{space 3}0.246{col 86}{space 4}-.1852919{col 99}{space 3} .7215346
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .4350422{col 58}{space 2} .2438204{col 69}{space 1}    1.78{col 78}{space 3}0.074{col 86}{space 4}-.0428424{col 99}{space 3} .9129269
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .4843341{col 58}{space 2} .2392668{col 69}{space 1}    2.02{col 78}{space 3}0.043{col 86}{space 4} .0153744{col 99}{space 3} .9532938
{txt}{space 39}2000  {c |}{col 46}{res}{space 2} .3945775{col 58}{space 2}  .171296{col 69}{space 1}    2.30{col 78}{space 3}0.021{col 86}{space 4} .0588396{col 99}{space 3} .7303154
{txt}{space 39}2001  {c |}{col 46}{res}{space 2} .3152664{col 58}{space 2} .2288301{col 69}{space 1}    1.38{col 78}{space 3}0.168{col 86}{space 4}-.1332374{col 99}{space 3} .7637702
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .5033247{col 58}{space 2} .2336668{col 69}{space 1}    2.15{col 78}{space 3}0.031{col 86}{space 4}  .045341{col 99}{space 3} .9613085
{txt}{space 39}2003  {c |}{col 46}{res}{space 2}-.1063939{col 58}{space 2} .2239375{col 69}{space 1}   -0.48{col 78}{space 3}0.635{col 86}{space 4}-.5453084{col 99}{space 3} .3325207
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} .3586572{col 58}{space 2} .1701907{col 69}{space 1}    2.11{col 78}{space 3}0.035{col 86}{space 4} .0250857{col 99}{space 3} .6922288
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}-.1007843{col 58}{space 2} .2222864{col 69}{space 1}   -0.45{col 78}{space 3}0.650{col 86}{space 4}-.5364626{col 99}{space 3}  .334894
{txt}{space 39}2006  {c |}{col 46}{res}{space 2} -.205738{col 58}{space 2} .2091943{col 69}{space 1}   -0.98{col 78}{space 3}0.325{col 86}{space 4}-.6157561{col 99}{space 3} .2042801
{txt}{space 39}2007  {c |}{col 46}{res}{space 2}  .060499{col 58}{space 2} .2008218{col 69}{space 1}    0.30{col 78}{space 3}0.763{col 86}{space 4} -.333109{col 99}{space 3} .4541069
{txt}{space 39}2008  {c |}{col 46}{res}{space 2}  .040753{col 58}{space 2} .1693129{col 69}{space 1}    0.24{col 78}{space 3}0.810{col 86}{space 4}-.2910979{col 99}{space 3} .3726039
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.3488436{col 58}{space 2}  .207738{col 69}{space 1}   -1.68{col 78}{space 3}0.093{col 86}{space 4}-.7560074{col 99}{space 3} .0583201
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}-.3522072{col 58}{space 2} .2460651{col 69}{space 1}   -1.43{col 78}{space 3}0.152{col 86}{space 4}-.8344914{col 99}{space 3}  .130077
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.2596055{col 58}{space 2} .2620862{col 69}{space 1}   -0.99{col 78}{space 3}0.322{col 86}{space 4}-.7732909{col 99}{space 3} .2540798
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .5867835{col 58}{space 2} .2612396{col 69}{space 1}    2.25{col 78}{space 3}0.025{col 86}{space 4} .0747574{col 99}{space 3}  1.09881
{txt}{space 39}2013  {c |}{col 46}{res}{space 2} .1999858{col 58}{space 2} .2536531{col 69}{space 1}    0.79{col 78}{space 3}0.430{col 86}{space 4}-.2971708{col 99}{space 3} .6971423
{txt}{space 39}2014  {c |}{col 46}{res}{space 2} .1921145{col 58}{space 2} .2724661{col 69}{space 1}    0.71{col 78}{space 3}0.481{col 86}{space 4}-.3419154{col 99}{space 3} .7261443
{txt}{space 39}2015  {c |}{col 46}{res}{space 2} .1402797{col 58}{space 2} .2729682{col 69}{space 1}    0.51{col 78}{space 3}0.607{col 86}{space 4}-.3947343{col 99}{space 3} .6752936
{txt}{space 39}2016  {c |}{col 46}{res}{space 2} .0650958{col 58}{space 2} .2549137{col 69}{space 1}    0.26{col 78}{space 3}0.798{col 86}{space 4}-.4345317{col 99}{space 3} .5647233
{txt}{space 39}2017  {c |}{col 46}{res}{space 2} .1018568{col 58}{space 2} .2574779{col 69}{space 1}    0.40{col 78}{space 3}0.692{col 86}{space 4}-.4027964{col 99}{space 3} .6065101
{txt}{space 39}2018  {c |}{col 46}{res}{space 2}-.0011326{col 58}{space 2} .2601924{col 69}{space 1}   -0.00{col 78}{space 3}0.997{col 86}{space 4}-.5111062{col 99}{space 3}  .508841
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.2213492{col 58}{space 2}   .21923{col 69}{space 1}   -1.01{col 78}{space 3}0.313{col 86}{space 4} -.651037{col 99}{space 3} .2083386
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}-.1436178{col 58}{space 2} .2250577{col 69}{space 1}   -0.64{col 78}{space 3}0.523{col 86}{space 4}-.5847278{col 99}{space 3} .2974922
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.2580538{col 58}{space 2} .2350346{col 69}{space 1}   -1.10{col 78}{space 3}0.272{col 86}{space 4}-.7187185{col 99}{space 3}  .202611
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.3190253{col 58}{space 2} .2115245{col 69}{space 1}   -1.51{col 78}{space 3}0.132{col 86}{space 4}-.7336105{col 99}{space 3} .0955598
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}   -.0705{col 58}{space 2} .0329647{col 69}{space 1}   -2.14{col 78}{space 3}0.032{col 86}{space 4}-.1351104{col 99}{space 3}-.0058896
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2} .0103905{col 58}{space 2} .0273801{col 69}{space 1}    0.38{col 78}{space 3}0.704{col 86}{space 4}-.0432741{col 99}{space 3} .0640551
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0615544{col 58}{space 2} .0196914{col 69}{space 1}    3.13{col 78}{space 3}0.002{col 86}{space 4} .0229595{col 99}{space 3} .1001493
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0385018{col 58}{space 2} .0228757{col 69}{space 1}    1.68{col 78}{space 3}0.092{col 86}{space 4}-.0063344{col 99}{space 3} .0833379
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2} .0032028{col 58}{space 2} .0241545{col 69}{space 1}    0.13{col 78}{space 3}0.895{col 86}{space 4}-.0441397{col 99}{space 3} .0505454
{txt}{space 38}avila  {c |}{col 46}{res}{space 2}-.0145465{col 58}{space 2} .0368622{col 69}{space 1}   -0.39{col 78}{space 3}0.693{col 86}{space 4} -.086796{col 99}{space 3}  .057703
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0189974{col 58}{space 2}   .02312{col 69}{space 1}    0.82{col 78}{space 3}0.411{col 86}{space 4}-.0263175{col 99}{space 3} .0643124
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.0299503{col 58}{space 2}  .021812{col 69}{space 1}   -1.37{col 78}{space 3}0.170{col 86}{space 4}-.0727015{col 99}{space 3}  .012801
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0351714{col 58}{space 2} .0324126{col 69}{space 1}    1.09{col 78}{space 3}0.278{col 86}{space 4}-.0283569{col 99}{space 3} .0986996
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2} .1097018{col 58}{space 2} .0281957{col 69}{space 1}    3.89{col 78}{space 3}0.000{col 86}{space 4} .0544386{col 99}{space 3} .1649649
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2} .0189856{col 58}{space 2} .0217939{col 69}{space 1}    0.87{col 78}{space 3}0.384{col 86}{space 4}-.0237301{col 99}{space 3} .0617013
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2}-.0066621{col 58}{space 2} .0221704{col 69}{space 1}   -0.30{col 78}{space 3}0.764{col 86}{space 4}-.0501157{col 99}{space 3} .0367915
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2} .0232023{col 58}{space 2} .0238792{col 69}{space 1}    0.97{col 78}{space 3}0.331{col 86}{space 4}-.0236006{col 99}{space 3} .0700051
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0313323{col 58}{space 2} .0279879{col 69}{space 1}    1.12{col 78}{space 3}0.263{col 86}{space 4}-.0235235{col 99}{space 3} .0861882
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2} .0110146{col 58}{space 2} .0222629{col 69}{space 1}    0.49{col 78}{space 3}0.621{col 86}{space 4}-.0326203{col 99}{space 3} .0546495
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2}-.0028361{col 58}{space 2} .0356677{col 69}{space 1}   -0.08{col 78}{space 3}0.937{col 86}{space 4}-.0727444{col 99}{space 3} .0670721
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.1202238{col 58}{space 2} .0262351{col 69}{space 1}   -4.58{col 78}{space 3}0.000{col 86}{space 4}-.1716443{col 99}{space 3}-.0688033
{txt}{space 37}girona  {c |}{col 46}{res}{space 2} -.093035{col 58}{space 2}  .028706{col 69}{space 1}   -3.24{col 78}{space 3}0.001{col 86}{space 4}-.1492985{col 99}{space 3}-.0367716
{txt}{space 36}granada  {c |}{col 46}{res}{space 2} .0509758{col 58}{space 2} .0224026{col 69}{space 1}    2.28{col 78}{space 3}0.023{col 86}{space 4}  .007067{col 99}{space 3} .0948846
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0485529{col 58}{space 2} .0291094{col 69}{space 1}    1.67{col 78}{space 3}0.095{col 86}{space 4}-.0085011{col 99}{space 3} .1056069
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2} .0142143{col 58}{space 2} .0296184{col 69}{space 1}    0.48{col 78}{space 3}0.631{col 86}{space 4}-.0438374{col 99}{space 3}  .072266
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2} .0004335{col 58}{space 2} .0341543{col 69}{space 1}    0.01{col 78}{space 3}0.990{col 86}{space 4}-.0665085{col 99}{space 3} .0673754
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2}-.0108829{col 58}{space 2} .0275187{col 69}{space 1}   -0.40{col 78}{space 3}0.692{col 86}{space 4}-.0648192{col 99}{space 3} .0430535
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0539664{col 58}{space 2} .0235825{col 69}{space 1}    2.29{col 78}{space 3}0.022{col 86}{space 4}  .007745{col 99}{space 3} .1001878
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2}  .053234{col 58}{space 2} .0260302{col 69}{space 1}    2.05{col 78}{space 3}0.041{col 86}{space 4} .0022152{col 99}{space 3} .1042528
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} .0224252{col 58}{space 2} .0253646{col 69}{space 1}    0.88{col 78}{space 3}0.377{col 86}{space 4} -.027289{col 99}{space 3} .0721394
{txt}{space 39}leon  {c |}{col 46}{res}{space 2}  .074986{col 58}{space 2}  .028367{col 69}{space 1}    2.64{col 78}{space 3}0.008{col 86}{space 4}  .019387{col 99}{space 3}  .130585
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2}-.0421215{col 58}{space 2} .0279971{col 69}{space 1}   -1.50{col 78}{space 3}0.132{col 86}{space 4}-.0969954{col 99}{space 3} .0127524
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2} .0058603{col 58}{space 2} .0275356{col 69}{space 1}    0.21{col 78}{space 3}0.831{col 86}{space 4}-.0481092{col 99}{space 3} .0598297
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2} .0182623{col 58}{space 2} .0189106{col 69}{space 1}    0.97{col 78}{space 3}0.334{col 86}{space 4}-.0188022{col 99}{space 3} .0553269
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0463066{col 58}{space 2} .0196816{col 69}{space 1}    2.35{col 78}{space 3}0.019{col 86}{space 4} .0077309{col 99}{space 3} .0848822
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2} .0341374{col 58}{space 2} .0196649{col 69}{space 1}    1.74{col 78}{space 3}0.083{col 86}{space 4}-.0044056{col 99}{space 3} .0726804
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0284012{col 58}{space 2}  .026047{col 69}{space 1}   -1.09{col 78}{space 3}0.276{col 86}{space 4} -.079453{col 99}{space 3} .0226507
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} .0281871{col 58}{space 2} .0294627{col 69}{space 1}    0.96{col 78}{space 3}0.339{col 86}{space 4}-.0295594{col 99}{space 3} .0859336
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2} .0440548{col 58}{space 2} .0361944{col 69}{space 1}    1.22{col 78}{space 3}0.224{col 86}{space 4}-.0268857{col 99}{space 3} .1149953
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2} .0217196{col 58}{space 2} .0215446{col 69}{space 1}    1.01{col 78}{space 3}0.313{col 86}{space 4}-.0205076{col 99}{space 3} .0639467
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2} .0181596{col 58}{space 2} .0291793{col 69}{space 1}    0.62{col 78}{space 3}0.534{col 86}{space 4}-.0390314{col 99}{space 3} .0753506
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2}  .065842{col 58}{space 2} .0216898{col 69}{space 1}    3.04{col 78}{space 3}0.002{col 86}{space 4} .0233302{col 99}{space 3} .1083537
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2} .0605168{col 58}{space 2} .0500013{col 69}{space 1}    1.21{col 78}{space 3}0.226{col 86}{space 4} -.037485{col 99}{space 3} .1585187
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2} .0235213{col 58}{space 2} .0215362{col 69}{space 1}    1.09{col 78}{space 3}0.275{col 86}{space 4}-.0186895{col 99}{space 3}  .065732
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .0927024{col 58}{space 2} .0800438{col 69}{space 1}    1.16{col 78}{space 3}0.247{col 86}{space 4}-.0641823{col 99}{space 3} .2495872
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2}-.0399481{col 58}{space 2} .0280698{col 69}{space 1}   -1.42{col 78}{space 3}0.155{col 86}{space 4}-.0949645{col 99}{space 3} .0150682
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2} .0343717{col 58}{space 2} .0387093{col 69}{space 1}    0.89{col 78}{space 3}0.375{col 86}{space 4} -.041498{col 99}{space 3} .1102413
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2} .0378888{col 58}{space 2} .0231215{col 69}{space 1}    1.64{col 78}{space 3}0.101{col 86}{space 4}-.0074291{col 99}{space 3} .0832066
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2} .0307773{col 58}{space 2} .0180852{col 69}{space 1}    1.70{col 78}{space 3}0.089{col 86}{space 4}-.0046695{col 99}{space 3} .0662241
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2} .0177237{col 58}{space 2} .0233672{col 69}{space 1}    0.76{col 78}{space 3}0.448{col 86}{space 4}-.0280757{col 99}{space 3} .0635231
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}  -.07942{col 58}{space 2} .0202726{col 69}{space 1}   -3.92{col 78}{space 3}0.000{col 86}{space 4} -.119154{col 99}{space 3} -.039686
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2}  .102479{col 58}{space 2} .0389868{col 69}{space 1}    2.63{col 78}{space 3}0.009{col 86}{space 4} .0260654{col 99}{space 3} .1788926
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2} .0254885{col 58}{space 2}  .022224{col 69}{space 1}    1.15{col 78}{space 3}0.251{col 86}{space 4}-.0180703{col 99}{space 3} .0690473
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0106497{col 58}{space 2} .0100616{col 69}{space 1}    1.06{col 78}{space 3}0.290{col 86}{space 4}-.0090709{col 99}{space 3} .0303704
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0055447{col 58}{space 2} .0097036{col 69}{space 1}    0.57{col 78}{space 3}0.568{col 86}{space 4}-.0134743{col 99}{space 3} .0245637
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0049555{col 58}{space 2} .0108634{col 69}{space 1}    0.46{col 78}{space 3}0.648{col 86}{space 4}-.0163365{col 99}{space 3} .0262475
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2}  .011749{col 58}{space 2} .0099637{col 69}{space 1}    1.18{col 78}{space 3}0.238{col 86}{space 4}-.0077798{col 99}{space 3} .0312777
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2} .0017265{col 58}{space 2} .0137819{col 69}{space 1}    0.13{col 78}{space 3}0.900{col 86}{space 4}-.0252858{col 99}{space 3} .0287389
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}  .009217{col 58}{space 2}  .013427{col 69}{space 1}    0.69{col 78}{space 3}0.492{col 86}{space 4}-.0170997{col 99}{space 3} .0355337
{txt}{space 44} {c |}
{space 41}age {c |}{col 46}{res}{space 2} .0019424{col 58}{space 2} .0002108{col 69}{space 1}    9.21{col 78}{space 3}0.000{col 86}{space 4} .0015292{col 99}{space 3} .0023556
{txt}{space 44} {c |}
{space 35}education {c |}
{space 34}Secondary  {c |}{col 46}{res}{space 2}-.0297952{col 58}{space 2} .0056124{col 69}{space 1}   -5.31{col 78}{space 3}0.000{col 86}{space 4}-.0407954{col 99}{space 3}-.0187949
{txt}{space 27}Higher Education  {c |}{col 46}{res}{space 2}-.0508852{col 58}{space 2} .0061485{col 69}{space 1}   -8.28{col 78}{space 3}0.000{col 86}{space 4}-.0629362{col 99}{space 3}-.0388341
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0090128{col 58}{space 2} .0069304{col 69}{space 1}    1.30{col 78}{space 3}0.193{col 86}{space 4}-.0045706{col 99}{space 3} .0225962
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0432768{col 58}{space 2} .0077093{col 69}{space 1}    5.61{col 78}{space 3}0.000{col 86}{space 4} .0281666{col 99}{space 3} .0583869
{txt}{space 36}Student  {c |}{col 46}{res}{space 2} .0076198{col 58}{space 2}  .011723{col 69}{space 1}    0.65{col 78}{space 3}0.516{col 86}{space 4}-.0153571{col 99}{space 3} .0305967
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2}-.0077275{col 58}{space 2} .0681207{col 69}{space 1}   -0.11{col 78}{space 3}0.910{col 86}{space 4}-.1412431{col 99}{space 3} .1257881
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2} .0237602{col 58}{space 2} .0166681{col 69}{space 1}    1.43{col 78}{space 3}0.154{col 86}{space 4} -.008909{col 99}{space 3} .0564295
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0105615{col 58}{space 2} .0147729{col 69}{space 1}    0.71{col 78}{space 3}0.475{col 86}{space 4}-.0183933{col 99}{space 3} .0395163
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0089456{col 58}{space 2} .0139699{col 69}{space 1}    0.64{col 78}{space 3}0.522{col 86}{space 4}-.0184353{col 99}{space 3} .0363264
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2}-.0112527{col 58}{space 2} .0149263{col 69}{space 1}   -0.75{col 78}{space 3}0.451{col 86}{space 4} -.040508{col 99}{space 3} .0180026
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0039423{col 58}{space 2} .0136748{col 69}{space 1}    0.29{col 78}{space 3}0.773{col 86}{space 4}-.0228601{col 99}{space 3} .0307447
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2} -.000218{col 58}{space 2} .0191797{col 69}{space 1}   -0.01{col 78}{space 3}0.991{col 86}{space 4}  -.03781{col 99}{space 3} .0373739
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2} -.000718{col 58}{space 2} .0002567{col 69}{space 1}   -2.80{col 78}{space 3}0.005{col 86}{space 4}-.0012212{col 99}{space 3}-.0002149
{txt}{space 44} {c |}
{space 25}preperiod#education {c |}
{space 19}1#Primary School or less  {c |}{col 46}{res}{space 2} -.023258{col 58}{space 2} .0079222{col 69}{space 1}   -2.94{col 78}{space 3}0.003{col 86}{space 4}-.0387855{col 99}{space 3}-.0077306
{txt}{space 32}1#Secondary  {c |}{col 46}{res}{space 2}-.0219314{col 58}{space 2} .0078415{col 69}{space 1}   -2.80{col 78}{space 3}0.005{col 86}{space 4}-.0373006{col 99}{space 3}-.0065622
{txt}{space 25}1#Higher Education  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2} .0087059{col 58}{space 2} .0734447{col 69}{space 1}    0.12{col 78}{space 3}0.906{col 86}{space 4}-.1352447{col 99}{space 3} .1526565
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2} -.019732{col 58}{space 2} .0738184{col 69}{space 1}   -0.27{col 78}{space 3}0.789{col 86}{space 4}-.1644151{col 99}{space 3} .1249512
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2}-.0132298{col 58}{space 2} .0737102{col 69}{space 1}   -0.18{col 78}{space 3}0.858{col 86}{space 4}-.1577009{col 99}{space 3} .1312412
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2}-.0126616{col 58}{space 2} .0746172{col 69}{space 1}   -0.17{col 78}{space 3}0.865{col 86}{space 4}-.1589103{col 99}{space 3} .1335871
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2} .0180554{col 58}{space 2} .0277277{col 69}{space 1}    0.65{col 78}{space 3}0.515{col 86}{space 4}-.0362906{col 99}{space 3} .0724014
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2}-.0168375{col 58}{space 2} .0391885{col 69}{space 1}   -0.43{col 78}{space 3}0.667{col 86}{space 4}-.0936464{col 99}{space 3} .0599714
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .0730425{col 58}{space 2} .0352261{col 69}{space 1}    2.07{col 78}{space 3}0.038{col 86}{space 4} .0039998{col 99}{space 3} .1420852
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0451907{col 58}{space 2}  .027849{col 69}{space 1}    1.62{col 78}{space 3}0.105{col 86}{space 4} -.009393{col 99}{space 3} .0997745
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0451608{col 58}{space 2} .0314364{col 69}{space 1}    1.44{col 78}{space 3}0.151{col 86}{space 4}-.0164542{col 99}{space 3} .1067758
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2} .0351383{col 58}{space 2}  .031732{col 69}{space 1}    1.11{col 78}{space 3}0.268{col 86}{space 4} -.027056{col 99}{space 3} .0973327
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0931998{col 58}{space 2} .0449563{col 69}{space 1}    2.07{col 78}{space 3}0.038{col 86}{space 4} .0050861{col 99}{space 3} .1813136
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0982819{col 58}{space 2} .0310894{col 69}{space 1}    3.16{col 78}{space 3}0.002{col 86}{space 4} .0373472{col 99}{space 3} .1592167
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2}   .02668{col 58}{space 2} .0271542{col 69}{space 1}    0.98{col 78}{space 3}0.326{col 86}{space 4}-.0265419{col 99}{space 3}  .079902
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0383431{col 58}{space 2} .0398944{col 69}{space 1}   -0.96{col 78}{space 3}0.336{col 86}{space 4}-.1165357{col 99}{space 3} .0398494
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2}  .005635{col 58}{space 2} .0357642{col 69}{space 1}    0.16{col 78}{space 3}0.875{col 86}{space 4}-.0644623{col 99}{space 3} .0757323
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .0976953{col 58}{space 2} .0301077{col 69}{space 1}    3.24{col 78}{space 3}0.001{col 86}{space 4} .0386846{col 99}{space 3}  .156706
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .0904263{col 58}{space 2} .0308616{col 69}{space 1}    2.93{col 78}{space 3}0.003{col 86}{space 4} .0299381{col 99}{space 3} .1509146
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .0849337{col 58}{space 2} .0317056{col 69}{space 1}    2.68{col 78}{space 3}0.007{col 86}{space 4} .0227912{col 99}{space 3} .1470762
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2} .0527591{col 58}{space 2} .0356627{col 69}{space 1}    1.48{col 78}{space 3}0.139{col 86}{space 4}-.0171394{col 99}{space 3} .1226575
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2}-.0288965{col 58}{space 2} .0300511{col 69}{space 1}   -0.96{col 78}{space 3}0.336{col 86}{space 4}-.0877961{col 99}{space 3} .0300032
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2} .0794063{col 58}{space 2} .0434027{col 69}{space 1}    1.83{col 78}{space 3}0.067{col 86}{space 4}-.0056625{col 99}{space 3}  .164475
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0328119{col 58}{space 2} .0323601{col 69}{space 1}   -1.01{col 78}{space 3}0.311{col 86}{space 4}-.0962372{col 99}{space 3} .0306135
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2} .0311098{col 58}{space 2} .0350359{col 69}{space 1}    0.89{col 78}{space 3}0.375{col 86}{space 4}-.0375601{col 99}{space 3} .0997798
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2} -.001955{col 58}{space 2} .0306173{col 69}{space 1}   -0.06{col 78}{space 3}0.949{col 86}{space 4}-.0619644{col 99}{space 3} .0580545
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2} .0511672{col 58}{space 2} .0376114{col 69}{space 1}    1.36{col 78}{space 3}0.174{col 86}{space 4}-.0225506{col 99}{space 3}  .124885
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} .0674285{col 58}{space 2} .0371842{col 69}{space 1}    1.81{col 78}{space 3}0.070{col 86}{space 4} -.005452{col 99}{space 3}  .140309
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2}  .011954{col 58}{space 2}  .041809{col 69}{space 1}    0.29{col 78}{space 3}0.775{col 86}{space 4} -.069991{col 99}{space 3} .0938991
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .0836484{col 58}{space 2} .0331396{col 69}{space 1}    2.52{col 78}{space 3}0.012{col 86}{space 4} .0186953{col 99}{space 3} .1486015
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2} .0019897{col 58}{space 2} .0315469{col 69}{space 1}    0.06{col 78}{space 3}0.950{col 86}{space 4}-.0598419{col 99}{space 3} .0638213
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0440205{col 58}{space 2} .0351497{col 69}{space 1}   -1.25{col 78}{space 3}0.210{col 86}{space 4}-.1129135{col 99}{space 3} .0248725
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .0864745{col 58}{space 2} .0323633{col 69}{space 1}    2.67{col 78}{space 3}0.008{col 86}{space 4} .0230429{col 99}{space 3} .1499061
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2}-.0343251{col 58}{space 2} .0359401{col 69}{space 1}   -0.96{col 78}{space 3}0.340{col 86}{space 4}-.1047672{col 99}{space 3} .0361169
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2}-.0521899{col 58}{space 2} .0369232{col 69}{space 1}   -1.41{col 78}{space 3}0.158{col 86}{space 4} -.124559{col 99}{space 3} .0201791
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0667337{col 58}{space 2} .0354329{col 69}{space 1}    1.88{col 78}{space 3}0.060{col 86}{space 4}-.0027143{col 99}{space 3} .1361818
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0077909{col 58}{space 2}  .025427{col 69}{space 1}    0.31{col 78}{space 3}0.759{col 86}{space 4}-.0420457{col 99}{space 3} .0576275
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .0746516{col 58}{space 2} .0255764{col 69}{space 1}    2.92{col 78}{space 3}0.004{col 86}{space 4} .0245223{col 99}{space 3}  .124781
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2} .0361939{col 58}{space 2} .0265162{col 69}{space 1}    1.36{col 78}{space 3}0.172{col 86}{space 4}-.0157774{col 99}{space 3} .0881653
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2} .0056377{col 58}{space 2} .0330698{col 69}{space 1}    0.17{col 78}{space 3}0.865{col 86}{space 4}-.0591786{col 99}{space 3} .0704539
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2} .0264399{col 58}{space 2} .0363157{col 69}{space 1}    0.73{col 78}{space 3}0.467{col 86}{space 4}-.0447383{col 99}{space 3} .0976182
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2} .0176575{col 58}{space 2} .0445943{col 69}{space 1}    0.40{col 78}{space 3}0.692{col 86}{space 4}-.0697466{col 99}{space 3} .1050617
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}-.0118733{col 58}{space 2} .0289742{col 69}{space 1}   -0.41{col 78}{space 3}0.682{col 86}{space 4}-.0686622{col 99}{space 3} .0449157
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .0909892{col 58}{space 2} .0369146{col 69}{space 1}    2.46{col 78}{space 3}0.014{col 86}{space 4} .0186371{col 99}{space 3} .1633414
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0029613{col 58}{space 2} .0296044{col 69}{space 1}   -0.10{col 78}{space 3}0.920{col 86}{space 4}-.0609855{col 99}{space 3} .0550628
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0627979{col 58}{space 2} .0568762{col 69}{space 1}   -1.10{col 78}{space 3}0.270{col 86}{space 4}-.1742745{col 99}{space 3} .0486787
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .0505836{col 58}{space 2}  .026573{col 69}{space 1}    1.90{col 78}{space 3}0.057{col 86}{space 4}-.0014992{col 99}{space 3} .1026664
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2}-.0225652{col 58}{space 2} .0864144{col 69}{space 1}   -0.26{col 78}{space 3}0.794{col 86}{space 4}-.1919362{col 99}{space 3} .1468059
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2} .0516423{col 58}{space 2} .0344947{col 69}{space 1}    1.50{col 78}{space 3}0.134{col 86}{space 4}-.0159668{col 99}{space 3} .1192515
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0231234{col 58}{space 2} .0453488{col 69}{space 1}    0.51{col 78}{space 3}0.610{col 86}{space 4}-.0657597{col 99}{space 3} .1120065
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2} .0333185{col 58}{space 2} .0317007{col 69}{space 1}    1.05{col 78}{space 3}0.293{col 86}{space 4}-.0288144{col 99}{space 3} .0954514
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2} .0341872{col 58}{space 2} .0239589{col 69}{space 1}    1.43{col 78}{space 3}0.154{col 86}{space 4}-.0127719{col 99}{space 3} .0811463
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2}  .025886{col 58}{space 2} .0317838{col 69}{space 1}    0.81{col 78}{space 3}0.415{col 86}{space 4}-.0364098{col 99}{space 3} .0881818
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0756764{col 58}{space 2} .0274994{col 69}{space 1}   -2.75{col 78}{space 3}0.006{col 86}{space 4}-.1295749{col 99}{space 3} -.021778
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2}  .004757{col 58}{space 2} .0464071{col 69}{space 1}    0.10{col 78}{space 3}0.918{col 86}{space 4}-.0862003{col 99}{space 3} .0957142
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1538824{col 58}{space 2} .0566676{col 69}{space 1}    2.72{col 78}{space 3}0.007{col 86}{space 4} .0428148{col 99}{space 3} .2649501
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}105349{txt}) = {res}5.337{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.2811
{col 25}{txt}Prob>|t| = {res}    0.8038

95%{txt} confidence set for null hypothesis expression: {res}[−.01201, .01627]
{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_20_21_male.tex"'})
(951,515 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:73,901}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:86}, {res:73780})}{col 70} = {res}{ralign 6:26.47}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0378}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0362}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4350}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}  .007226{col 56}{space 2} .0058687{col 67}{space 1}    1.23{col 76}{space 3}0.218{col 84}{space 4}-.0042766{col 97}{space 3} .0187286
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0908138{col 56}{space 2} .1676904{col 67}{space 1}   -0.54{col 76}{space 3}0.588{col 84}{space 4}-.4194863{col 97}{space 3} .2378588
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}-.0170646{col 56}{space 2} .2345491{col 67}{space 1}   -0.07{col 76}{space 3}0.942{col 84}{space 4}  -.47678{col 97}{space 3} .4426508
{txt}{space 37}1989  {c |}{col 44}{res}{space 2}-.1800169{col 56}{space 2} .1923538{col 67}{space 1}   -0.94{col 76}{space 3}0.349{col 84}{space 4}-.5570295{col 97}{space 3} .1969958
{txt}{space 37}1990  {c |}{col 44}{res}{space 2}-.2219052{col 56}{space 2} .1917845{col 67}{space 1}   -1.16{col 76}{space 3}0.247{col 84}{space 4}-.5978021{col 97}{space 3} .1539917
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.2117617{col 56}{space 2} .2369646{col 67}{space 1}   -0.89{col 76}{space 3}0.372{col 84}{space 4}-.6762114{col 97}{space 3}  .252688
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.1782533{col 56}{space 2} .2010693{col 67}{space 1}   -0.89{col 76}{space 3}0.375{col 84}{space 4}-.5723483{col 97}{space 3} .2158418
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}-.0196779{col 56}{space 2} .1885887{col 67}{space 1}   -0.10{col 76}{space 3}0.917{col 84}{space 4} -.389311{col 97}{space 3} .3499552
{txt}{space 37}1994  {c |}{col 44}{res}{space 2} .0157495{col 56}{space 2} .1912946{col 67}{space 1}    0.08{col 76}{space 3}0.934{col 84}{space 4}-.3591873{col 97}{space 3} .3906862
{txt}{space 37}1995  {c |}{col 44}{res}{space 2}-.0892865{col 56}{space 2} .1868789{col 67}{space 1}   -0.48{col 76}{space 3}0.633{col 84}{space 4}-.4555684{col 97}{space 3} .2769954
{txt}{space 37}1996  {c |}{col 44}{res}{space 2}-.0473191{col 56}{space 2} .2031583{col 67}{space 1}   -0.23{col 76}{space 3}0.816{col 84}{space 4}-.4455086{col 97}{space 3} .3508704
{txt}{space 37}1997  {c |}{col 44}{res}{space 2} .6187178{col 56}{space 2} .2296475{col 67}{space 1}    2.69{col 76}{space 3}0.007{col 84}{space 4} .1686096{col 97}{space 3} 1.068826
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .3205215{col 56}{space 2}  .242948{col 67}{space 1}    1.32{col 76}{space 3}0.187{col 84}{space 4}-.1556557{col 97}{space 3} .7966987
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .3046634{col 56}{space 2} .2392208{col 67}{space 1}    1.27{col 76}{space 3}0.203{col 84}{space 4}-.1642084{col 97}{space 3} .7735352
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .5069566{col 56}{space 2}   .17006{col 67}{space 1}    2.98{col 76}{space 3}0.003{col 84}{space 4} .1736396{col 97}{space 3} .8402735
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} .1871769{col 56}{space 2} .2272189{col 67}{space 1}    0.82{col 76}{space 3}0.410{col 84}{space 4}-.2581711{col 97}{space 3}  .632525
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} .1727211{col 56}{space 2} .2323569{col 67}{space 1}    0.74{col 76}{space 3}0.457{col 84}{space 4}-.2826975{col 97}{space 3} .6281397
{txt}{space 37}2003  {c |}{col 44}{res}{space 2} .3042316{col 56}{space 2} .2215976{col 67}{space 1}    1.37{col 76}{space 3}0.170{col 84}{space 4}-.1300989{col 97}{space 3} .7385621
{txt}{space 37}2004  {c |}{col 44}{res}{space 2} .3880811{col 56}{space 2} .1689744{col 67}{space 1}    2.30{col 76}{space 3}0.022{col 84}{space 4} .0568919{col 97}{space 3} .7192703
{txt}{space 37}2005  {c |}{col 44}{res}{space 2}-.3343158{col 56}{space 2} .2233791{col 67}{space 1}   -1.50{col 76}{space 3}0.134{col 84}{space 4}-.7721379{col 97}{space 3} .1035063
{txt}{space 37}2006  {c |}{col 44}{res}{space 2} -.125548{col 56}{space 2}  .209545{col 67}{space 1}   -0.60{col 76}{space 3}0.549{col 84}{space 4}-.5362554{col 97}{space 3} .2851595
{txt}{space 37}2007  {c |}{col 44}{res}{space 2} -.016683{col 56}{space 2}  .201773{col 67}{space 1}   -0.08{col 76}{space 3}0.934{col 84}{space 4}-.4121572{col 97}{space 3} .3787913
{txt}{space 37}2008  {c |}{col 44}{res}{space 2} .0348536{col 56}{space 2} .1681532{col 67}{space 1}    0.21{col 76}{space 3}0.836{col 84}{space 4}-.2947261{col 97}{space 3} .3644333
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}-.2672861{col 56}{space 2} .2091965{col 67}{space 1}   -1.28{col 76}{space 3}0.201{col 84}{space 4}-.6773104{col 97}{space 3} .1427382
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1109586{col 56}{space 2} .0179807{col 67}{space 1}   -6.17{col 76}{space 3}0.000{col 84}{space 4}-.1462008{col 97}{space 3}-.0757164
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0569571{col 56}{space 2} .0177143{col 67}{space 1}    3.22{col 76}{space 3}0.001{col 84}{space 4} .0222371{col 97}{space 3} .0916771
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .1015506{col 56}{space 2} .0139223{col 67}{space 1}    7.29{col 76}{space 3}0.000{col 84}{space 4} .0742629{col 97}{space 3} .1288383
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0685823{col 56}{space 2} .0167994{col 67}{space 1}    4.08{col 76}{space 3}0.000{col 84}{space 4} .0356555{col 97}{space 3} .1015091
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0040177{col 56}{space 2} .0139513{col 67}{space 1}    0.29{col 76}{space 3}0.773{col 84}{space 4}-.0233267{col 97}{space 3} .0313622
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0482838{col 56}{space 2} .0211683{col 67}{space 1}    2.28{col 76}{space 3}0.023{col 84}{space 4}  .006794{col 97}{space 3} .0897735
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .1073859{col 56}{space 2} .0163522{col 67}{space 1}    6.57{col 76}{space 3}0.000{col 84}{space 4} .0753356{col 97}{space 3} .1394361
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0106204{col 56}{space 2} .0116764{col 67}{space 1}   -0.91{col 76}{space 3}0.363{col 84}{space 4}-.0335061{col 97}{space 3} .0122653
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0360042{col 56}{space 2} .0178666{col 67}{space 1}    2.02{col 76}{space 3}0.044{col 84}{space 4} .0009857{col 97}{space 3} .0710227
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0715429{col 56}{space 2} .0170488{col 67}{space 1}    4.20{col 76}{space 3}0.000{col 84}{space 4} .0381272{col 97}{space 3} .1049586
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0423755{col 56}{space 2} .0162448{col 67}{space 1}    2.61{col 76}{space 3}0.009{col 84}{space 4} .0105358{col 97}{space 3} .0742152
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0892231{col 56}{space 2} .0161708{col 67}{space 1}    5.52{col 76}{space 3}0.000{col 84}{space 4} .0575285{col 97}{space 3} .1209177
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0779419{col 56}{space 2} .0163648{col 67}{space 1}    4.76{col 76}{space 3}0.000{col 84}{space 4} .0458669{col 97}{space 3} .1100168
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0763488{col 56}{space 2} .0173718{col 67}{space 1}    4.39{col 76}{space 3}0.000{col 84}{space 4} .0423002{col 97}{space 3} .1103974
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0226506{col 56}{space 2} .0150436{col 67}{space 1}   -1.51{col 76}{space 3}0.132{col 84}{space 4} -.052136{col 97}{space 3} .0068349
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0537326{col 56}{space 2} .0204518{col 67}{space 1}    2.63{col 76}{space 3}0.009{col 84}{space 4} .0136471{col 97}{space 3} .0938181
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1768012{col 56}{space 2}  .015017{col 67}{space 1}  -11.77{col 76}{space 3}0.000{col 84}{space 4}-.2062344{col 97}{space 3}-.1473679
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0717101{col 56}{space 2}  .017153{col 67}{space 1}   -4.18{col 76}{space 3}0.000{col 84}{space 4}-.1053299{col 97}{space 3}-.0380902
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0339148{col 56}{space 2} .0152061{col 67}{space 1}    2.23{col 76}{space 3}0.026{col 84}{space 4} .0041109{col 97}{space 3} .0637187
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0181107{col 56}{space 2} .0204751{col 67}{space 1}    0.88{col 76}{space 3}0.376{col 84}{space 4}-.0220203{col 97}{space 3} .0582418
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0473851{col 56}{space 2} .0190786{col 67}{space 1}    2.48{col 76}{space 3}0.013{col 84}{space 4}  .009991{col 97}{space 3} .0847791
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0105926{col 56}{space 2} .0195456{col 67}{space 1}   -0.54{col 76}{space 3}0.588{col 84}{space 4} -.048902{col 97}{space 3} .0277168
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}  .057198{col 56}{space 2} .0164587{col 67}{space 1}    3.48{col 76}{space 3}0.001{col 84}{space 4}  .024939{col 97}{space 3} .0894571
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2}  .033937{col 56}{space 2} .0155864{col 67}{space 1}    2.18{col 76}{space 3}0.029{col 84}{space 4} .0033878{col 97}{space 3} .0644863
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2}-.0201897{col 56}{space 2} .0184918{col 67}{space 1}   -1.09{col 76}{space 3}0.275{col 84}{space 4}-.0564336{col 97}{space 3} .0160541
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0668539{col 56}{space 2} .0163961{col 67}{space 1}    4.08{col 76}{space 3}0.000{col 84}{space 4} .0347175{col 97}{space 3} .0989902
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0175117{col 56}{space 2} .0166231{col 67}{space 1}    1.05{col 76}{space 3}0.292{col 84}{space 4}-.0150695{col 97}{space 3} .0500928
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0807351{col 56}{space 2} .0194702{col 67}{space 1}   -4.15{col 76}{space 3}0.000{col 84}{space 4}-.1188966{col 97}{space 3}-.0425736
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0323082{col 56}{space 2} .0171629{col 67}{space 1}    1.88{col 76}{space 3}0.060{col 84}{space 4} -.001331{col 97}{space 3} .0659475
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0330586{col 56}{space 2} .0122522{col 67}{space 1}    2.70{col 76}{space 3}0.007{col 84}{space 4} .0090443{col 97}{space 3}  .057073
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2}  .094937{col 56}{space 2} .0145615{col 67}{space 1}    6.52{col 76}{space 3}0.000{col 84}{space 4} .0663965{col 97}{space 3} .1234776
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0327246{col 56}{space 2} .0141972{col 67}{space 1}    2.30{col 76}{space 3}0.021{col 84}{space 4} .0048981{col 97}{space 3} .0605511
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0610286{col 56}{space 2} .0167378{col 67}{space 1}   -3.65{col 76}{space 3}0.000{col 84}{space 4}-.0938347{col 97}{space 3}-.0282225
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0417196{col 56}{space 2} .0171589{col 67}{space 1}    2.43{col 76}{space 3}0.015{col 84}{space 4} .0080881{col 97}{space 3}  .075351
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0196499{col 56}{space 2} .0215533{col 67}{space 1}    0.91{col 76}{space 3}0.362{col 84}{space 4}-.0225945{col 97}{space 3} .0618943
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0245309{col 56}{space 2} .0143951{col 67}{space 1}   -1.70{col 76}{space 3}0.088{col 84}{space 4}-.0527452{col 97}{space 3} .0036835
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .1129371{col 56}{space 2}  .017499{col 67}{space 1}    6.45{col 76}{space 3}0.000{col 84}{space 4}  .078639{col 97}{space 3} .1472351
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2}  .018427{col 56}{space 2}  .015404{col 67}{space 1}    1.20{col 76}{space 3}0.232{col 84}{space 4}-.0117647{col 97}{space 3} .0486187
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0205423{col 56}{space 2}  .021993{col 67}{space 1}    0.93{col 76}{space 3}0.350{col 84}{space 4}-.0225639{col 97}{space 3} .0636485
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .1001988{col 56}{space 2} .0144863{col 67}{space 1}    6.92{col 76}{space 3}0.000{col 84}{space 4} .0718058{col 97}{space 3} .1285918
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0560706{col 56}{space 2} .0264924{col 67}{space 1}    2.12{col 76}{space 3}0.034{col 84}{space 4} .0041456{col 97}{space 3} .1079956
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0222274{col 56}{space 2} .0167825{col 67}{space 1}   -1.32{col 76}{space 3}0.185{col 84}{space 4}-.0551211{col 97}{space 3} .0106664
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0004538{col 56}{space 2} .0193403{col 67}{space 1}   -0.02{col 76}{space 3}0.981{col 84}{space 4}-.0383607{col 97}{space 3}  .037453
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0523121{col 56}{space 2} .0171476{col 67}{space 1}    3.05{col 76}{space 3}0.002{col 84}{space 4} .0187029{col 97}{space 3} .0859212
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0659084{col 56}{space 2} .0133251{col 67}{space 1}    4.95{col 76}{space 3}0.000{col 84}{space 4} .0397912{col 97}{space 3} .0920256
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0528371{col 56}{space 2} .0173305{col 67}{space 1}    3.05{col 76}{space 3}0.002{col 84}{space 4} .0188693{col 97}{space 3} .0868048
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1759487{col 56}{space 2} .0136268{col 67}{space 1}  -12.91{col 76}{space 3}0.000{col 84}{space 4}-.2026573{col 97}{space 3}-.1492402
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0949683{col 56}{space 2} .0204693{col 67}{space 1}    4.64{col 76}{space 3}0.000{col 84}{space 4} .0548485{col 97}{space 3} .1350881
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0087612{col 56}{space 2} .0163756{col 67}{space 1}    0.54{col 76}{space 3}0.593{col 84}{space 4}-.0233349{col 97}{space 3} .0408573
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0060638{col 56}{space 2} .0066193{col 67}{space 1}   -0.92{col 76}{space 3}0.360{col 84}{space 4}-.0190376{col 97}{space 3} .0069101
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0066133{col 56}{space 2} .0065479{col 67}{space 1}   -1.01{col 76}{space 3}0.313{col 84}{space 4}-.0194472{col 97}{space 3} .0062205
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} -.018417{col 56}{space 2} .0076973{col 67}{space 1}   -2.39{col 76}{space 3}0.017{col 84}{space 4}-.0335038{col 97}{space 3}-.0033303
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0022465{col 56}{space 2} .0066901{col 67}{space 1}    0.34{col 76}{space 3}0.737{col 84}{space 4}-.0108661{col 97}{space 3} .0153591
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0665095{col 56}{space 2} .0104524{col 67}{space 1}   -6.36{col 76}{space 3}0.000{col 84}{space 4}-.0869961{col 97}{space 3}-.0460229
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0317566{col 56}{space 2} .0096779{col 67}{space 1}   -3.28{col 76}{space 3}0.001{col 84}{space 4}-.0507252{col 97}{space 3}-.0127881
{txt}{space 42} {c |}
{space 39}age {c |}{col 44}{res}{space 2} .0007553{col 56}{space 2} .0001334{col 67}{space 1}    5.66{col 76}{space 3}0.000{col 84}{space 4} .0004939{col 97}{space 3} .0010167
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0050413{col 56}{space 2} .0046293{col 67}{space 1}   -1.09{col 76}{space 3}0.276{col 84}{space 4}-.0141147{col 97}{space 3} .0040321
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0108543{col 56}{space 2} .0055831{col 67}{space 1}   -1.94{col 76}{space 3}0.052{col 84}{space 4}-.0217971{col 97}{space 3} .0000886
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0078848{col 56}{space 2} .0058816{col 67}{space 1}   -1.34{col 76}{space 3}0.180{col 84}{space 4}-.0194127{col 97}{space 3}  .003643
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0191666{col 56}{space 2} .0066272{col 67}{space 1}    2.89{col 76}{space 3}0.004{col 84}{space 4} .0061774{col 97}{space 3} .0321559
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0343383{col 56}{space 2} .0072622{col 67}{space 1}   -4.73{col 76}{space 3}0.000{col 84}{space 4}-.0485722{col 97}{space 3}-.0201044
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0159962{col 56}{space 2} .0046726{col 67}{space 1}    3.42{col 76}{space 3}0.001{col 84}{space 4} .0068379{col 97}{space 3} .0251545
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2054658{col 56}{space 2} .0190712{col 67}{space 1}   10.77{col 76}{space 3}0.000{col 84}{space 4} .1680864{col 97}{space 3} .2428452
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}73780{txt}) = {res}9.150{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.6547
{col 25}{txt}Prob>|t| = {res}    0.5215

95%{txt} confidence set for null hypothesis expression: {res}[−.01866, .03701]
{txt}{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:38,157}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:76}, {res:38060})}{col 70} = {res}{ralign 6:12.01}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0343}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0319}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4022}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0016399{col 56}{space 2} .0071379{col 67}{space 1}   -0.23{col 76}{space 3}0.818{col 84}{space 4}-.0156304{col 97}{space 3} .0123506
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.4057526{col 56}{space 2} .1670951{col 67}{space 1}   -2.43{col 76}{space 3}0.015{col 84}{space 4}-.7332633{col 97}{space 3}-.0782419
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}-.0288179{col 56}{space 2} .2038722{col 67}{space 1}   -0.14{col 76}{space 3}0.888{col 84}{space 4}-.4284129{col 97}{space 3}  .370777
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .5502376{col 56}{space 2} .2013743{col 67}{space 1}    2.73{col 76}{space 3}0.006{col 84}{space 4} .1555387{col 97}{space 3} .9449365
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .4397355{col 56}{space 2} .1916292{col 67}{space 1}    2.29{col 76}{space 3}0.022{col 84}{space 4} .0641372{col 97}{space 3} .8153339
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .3283307{col 56}{space 2} .2080549{col 67}{space 1}    1.58{col 76}{space 3}0.115{col 84}{space 4}-.0794624{col 97}{space 3} .7361237
{txt}{space 37}2015  {c |}{col 44}{res}{space 2}  .676113{col 56}{space 2} .2082723{col 67}{space 1}    3.25{col 76}{space 3}0.001{col 84}{space 4} .2678937{col 97}{space 3} 1.084332
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .4262718{col 56}{space 2} .1849285{col 67}{space 1}    2.31{col 76}{space 3}0.021{col 84}{space 4} .0638071{col 97}{space 3} .7887365
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .7615883{col 56}{space 2} .1940845{col 67}{space 1}    3.92{col 76}{space 3}0.000{col 84}{space 4} .3811775{col 97}{space 3} 1.141999
{txt}{space 37}2018  {c |}{col 44}{res}{space 2} .6135601{col 56}{space 2} .1959303{col 67}{space 1}    3.13{col 76}{space 3}0.002{col 84}{space 4} .2295316{col 97}{space 3} .9975886
{txt}{space 37}2019  {c |}{col 44}{res}{space 2} .1233632{col 56}{space 2} .1435139{col 67}{space 1}    0.86{col 76}{space 3}0.390{col 84}{space 4}-.1579278{col 97}{space 3} .4046542
{txt}{space 37}2020  {c |}{col 44}{res}{space 2}  .269057{col 56}{space 2} .1519237{col 67}{space 1}    1.77{col 76}{space 3}0.077{col 84}{space 4}-.0287174{col 97}{space 3} .5668314
{txt}{space 37}2021  {c |}{col 44}{res}{space 2}-.0414528{col 56}{space 2} .1589003{col 67}{space 1}   -0.26{col 76}{space 3}0.794{col 84}{space 4}-.3529015{col 97}{space 3}  .269996
{txt}{space 37}2022  {c |}{col 44}{res}{space 2} .1595775{col 56}{space 2} .1476486{col 67}{space 1}    1.08{col 76}{space 3}0.280{col 84}{space 4}-.1298177{col 97}{space 3} .4489728
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1092508{col 56}{space 2} .0308315{col 67}{space 1}   -3.54{col 76}{space 3}0.000{col 84}{space 4}-.1696813{col 97}{space 3}-.0488203
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2}-.0180319{col 56}{space 2}  .026292{col 67}{space 1}   -0.69{col 76}{space 3}0.493{col 84}{space 4}-.0695649{col 97}{space 3} .0335011
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0308955{col 56}{space 2} .0185413{col 67}{space 1}    1.67{col 76}{space 3}0.096{col 84}{space 4}-.0054459{col 97}{space 3} .0672369
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0183735{col 56}{space 2} .0214071{col 67}{space 1}    0.86{col 76}{space 3}0.391{col 84}{space 4}-.0235849{col 97}{space 3}  .060332
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2}-.0019063{col 56}{space 2} .0222381{col 67}{space 1}   -0.09{col 76}{space 3}0.932{col 84}{space 4}-.0454936{col 97}{space 3}  .041681
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}-.0178743{col 56}{space 2} .0352951{col 67}{space 1}   -0.51{col 76}{space 3}0.613{col 84}{space 4}-.0870535{col 97}{space 3}  .051305
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0576897{col 56}{space 2} .0219573{col 67}{space 1}    2.63{col 76}{space 3}0.009{col 84}{space 4} .0146527{col 97}{space 3} .1007266
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0721462{col 56}{space 2}  .020344{col 67}{space 1}   -3.55{col 76}{space 3}0.000{col 84}{space 4} -.112021{col 97}{space 3}-.0322714
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0316225{col 56}{space 2} .0304269{col 67}{space 1}    1.04{col 76}{space 3}0.299{col 84}{space 4} -.028015{col 97}{space 3} .0912601
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0597167{col 56}{space 2} .0267418{col 67}{space 1}    2.23{col 76}{space 3}0.026{col 84}{space 4} .0073021{col 97}{space 3} .1121313
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2}-.0206344{col 56}{space 2} .0202514{col 67}{space 1}   -1.02{col 76}{space 3}0.308{col 84}{space 4}-.0603277{col 97}{space 3}  .019059
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2}-.0047954{col 56}{space 2} .0206427{col 67}{space 1}   -0.23{col 76}{space 3}0.816{col 84}{space 4}-.0452557{col 97}{space 3} .0356648
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2}-.0006418{col 56}{space 2} .0221979{col 67}{space 1}   -0.03{col 76}{space 3}0.977{col 84}{space 4}-.0441502{col 97}{space 3} .0428666
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0222919{col 56}{space 2} .0258917{col 67}{space 1}    0.86{col 76}{space 3}0.389{col 84}{space 4}-.0284564{col 97}{space 3} .0730403
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0299794{col 56}{space 2} .0207188{col 67}{space 1}   -1.45{col 76}{space 3}0.148{col 84}{space 4}-.0705888{col 97}{space 3} .0106301
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0340242{col 56}{space 2} .0345886{col 67}{space 1}    0.98{col 76}{space 3}0.325{col 84}{space 4}-.0337704{col 97}{space 3} .1018187
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1298726{col 56}{space 2} .0241344{col 67}{space 1}   -5.38{col 76}{space 3}0.000{col 84}{space 4}-.1771767{col 97}{space 3}-.0825686
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.1142102{col 56}{space 2} .0268363{col 67}{space 1}   -4.26{col 76}{space 3}0.000{col 84}{space 4}-.1668101{col 97}{space 3}-.0616104
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0388816{col 56}{space 2} .0206494{col 67}{space 1}    1.88{col 76}{space 3}0.060{col 84}{space 4}-.0015918{col 97}{space 3}  .079355
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0778383{col 56}{space 2} .0283809{col 67}{space 1}    2.74{col 76}{space 3}0.006{col 84}{space 4}  .022211{col 97}{space 3} .1334656
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0346664{col 56}{space 2} .0278412{col 67}{space 1}    1.25{col 76}{space 3}0.213{col 84}{space 4}-.0199031{col 97}{space 3} .0892359
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0110876{col 56}{space 2} .0318399{col 67}{space 1}    0.35{col 76}{space 3}0.728{col 84}{space 4}-.0513194{col 97}{space 3} .0734946
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}-.0096193{col 56}{space 2} .0258501{col 67}{space 1}   -0.37{col 76}{space 3}0.710{col 84}{space 4}-.0602863{col 97}{space 3} .0410477
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0010153{col 56}{space 2} .0221696{col 67}{space 1}    0.05{col 76}{space 3}0.963{col 84}{space 4}-.0424377{col 97}{space 3} .0444683
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0498988{col 56}{space 2} .0240161{col 67}{space 1}    2.08{col 76}{space 3}0.038{col 84}{space 4} .0028266{col 97}{space 3}  .096971
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2}-.0077878{col 56}{space 2} .0236107{col 67}{space 1}   -0.33{col 76}{space 3}0.742{col 84}{space 4}-.0540653{col 97}{space 3} .0384897
{txt}{space 37}leon  {c |}{col 44}{res}{space 2}-.0009055{col 56}{space 2} .0262277{col 67}{space 1}   -0.03{col 76}{space 3}0.972{col 84}{space 4}-.0523125{col 97}{space 3} .0505015
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0858685{col 56}{space 2} .0260783{col 67}{space 1}   -3.29{col 76}{space 3}0.001{col 84}{space 4}-.1369827{col 97}{space 3}-.0347543
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}-.0044554{col 56}{space 2} .0250503{col 67}{space 1}   -0.18{col 76}{space 3}0.859{col 84}{space 4}-.0535547{col 97}{space 3}  .044644
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2}-.0217238{col 56}{space 2} .0175581{col 67}{space 1}   -1.24{col 76}{space 3}0.216{col 84}{space 4}-.0561382{col 97}{space 3} .0126906
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0215139{col 56}{space 2} .0185557{col 67}{space 1}    1.16{col 76}{space 3}0.246{col 84}{space 4}-.0148558{col 97}{space 3} .0578836
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} -.006458{col 56}{space 2}  .018419{col 67}{space 1}   -0.35{col 76}{space 3}0.726{col 84}{space 4}-.0425596{col 97}{space 3} .0296436
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0403061{col 56}{space 2} .0243793{col 67}{space 1}   -1.65{col 76}{space 3}0.098{col 84}{space 4}-.0880902{col 97}{space 3}  .007478
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2}-.0043216{col 56}{space 2} .0271767{col 67}{space 1}   -0.16{col 76}{space 3}0.874{col 84}{space 4}-.0575885{col 97}{space 3} .0489454
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2}-.0061717{col 56}{space 2} .0345978{col 67}{space 1}   -0.18{col 76}{space 3}0.858{col 84}{space 4}-.0739844{col 97}{space 3} .0616409
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0250617{col 56}{space 2}  .019592{col 67}{space 1}   -1.28{col 76}{space 3}0.201{col 84}{space 4}-.0634625{col 97}{space 3} .0133392
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0251331{col 56}{space 2} .0271154{col 67}{space 1}    0.93{col 76}{space 3}0.354{col 84}{space 4}-.0280139{col 97}{space 3}   .07828
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0370433{col 56}{space 2} .0199661{col 67}{space 1}    1.86{col 76}{space 3}0.064{col 84}{space 4}-.0020909{col 97}{space 3} .0761774
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0717883{col 56}{space 2} .0461812{col 67}{space 1}    1.55{col 76}{space 3}0.120{col 84}{space 4} -.018728{col 97}{space 3} .1623046
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2}-.0178055{col 56}{space 2} .0201512{col 67}{space 1}   -0.88{col 76}{space 3}0.377{col 84}{space 4}-.0573024{col 97}{space 3} .0216913
{txt}{space 36}soria  {c |}{col 44}{res}{space 2}  .132254{col 56}{space 2} .0755894{col 67}{space 1}    1.75{col 76}{space 3}0.080{col 84}{space 4}-.0159033{col 97}{space 3} .2804113
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0806298{col 56}{space 2} .0261972{col 67}{space 1}   -3.08{col 76}{space 3}0.002{col 84}{space 4}-.1319769{col 97}{space 3}-.0292826
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0425937{col 56}{space 2} .0356811{col 67}{space 1}   -1.19{col 76}{space 3}0.233{col 84}{space 4}-.1125297{col 97}{space 3} .0273423
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0493859{col 56}{space 2} .0218788{col 67}{space 1}    2.26{col 76}{space 3}0.024{col 84}{space 4}  .006503{col 97}{space 3} .0922688
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0114403{col 56}{space 2} .0168375{col 67}{space 1}    0.68{col 76}{space 3}0.497{col 84}{space 4}-.0215616{col 97}{space 3} .0444422
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}-.0213566{col 56}{space 2} .0218197{col 67}{space 1}   -0.98{col 76}{space 3}0.328{col 84}{space 4}-.0641238{col 97}{space 3} .0214105
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1066546{col 56}{space 2} .0188306{col 67}{space 1}   -5.66{col 76}{space 3}0.000{col 84}{space 4}-.1435631{col 97}{space 3}-.0697461
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0592457{col 56}{space 2} .0357134{col 67}{space 1}    1.66{col 76}{space 3}0.097{col 84}{space 4}-.0107535{col 97}{space 3} .1292449
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2}-.0047371{col 56}{space 2} .0207096{col 67}{space 1}   -0.23{col 76}{space 3}0.819{col 84}{space 4}-.0453284{col 97}{space 3} .0358542
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0104988{col 56}{space 2}   .00981{col 67}{space 1}   -1.07{col 76}{space 3}0.285{col 84}{space 4}-.0297267{col 97}{space 3} .0087291
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0055079{col 56}{space 2} .0094348{col 67}{space 1}   -0.58{col 76}{space 3}0.559{col 84}{space 4}-.0240004{col 97}{space 3} .0129846
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0045835{col 56}{space 2} .0104443{col 67}{space 1}    0.44{col 76}{space 3}0.661{col 84}{space 4}-.0158876{col 97}{space 3} .0250546
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0104624{col 56}{space 2} .0096166{col 67}{space 1}    1.09{col 76}{space 3}0.277{col 84}{space 4}-.0083864{col 97}{space 3} .0293113
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} -.017126{col 56}{space 2} .0131137{col 67}{space 1}   -1.31{col 76}{space 3}0.192{col 84}{space 4}-.0428292{col 97}{space 3} .0085771
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}  .018917{col 56}{space 2}  .012565{col 67}{space 1}    1.51{col 76}{space 3}0.132{col 84}{space 4}-.0057108{col 97}{space 3} .0435447
{txt}{space 42} {c |}
{space 39}age {c |}{col 44}{res}{space 2} .0015824{col 56}{space 2} .0001913{col 67}{space 1}    8.27{col 76}{space 3}0.000{col 84}{space 4} .0012075{col 97}{space 3} .0019574
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0241725{col 56}{space 2} .0055082{col 67}{space 1}   -4.39{col 76}{space 3}0.000{col 84}{space 4}-.0349687{col 97}{space 3}-.0133764
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0353568{col 56}{space 2} .0058387{col 67}{space 1}   -6.06{col 76}{space 3}0.000{col 84}{space 4}-.0468008{col 97}{space 3}-.0239127
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0074017{col 56}{space 2}  .006195{col 67}{space 1}    1.19{col 76}{space 3}0.232{col 84}{space 4}-.0047406{col 97}{space 3} .0195441
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0334428{col 56}{space 2} .0075329{col 67}{space 1}    4.44{col 76}{space 3}0.000{col 84}{space 4} .0186781{col 97}{space 3} .0482075
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0311325{col 56}{space 2} .0113662{col 67}{space 1}    2.74{col 76}{space 3}0.006{col 84}{space 4} .0088545{col 97}{space 3} .0534105
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0209936{col 56}{space 2} .0078897{col 67}{space 1}    2.66{col 76}{space 3}0.008{col 84}{space 4} .0055295{col 97}{space 3} .0364578
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1992475{col 56}{space 2} .0382762{col 67}{space 1}    5.21{col 76}{space 3}0.000{col 84}{space 4} .1242251{col 97}{space 3} .2742699
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}38060{txt}) = {res}8.627{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.2646
{col 25}{txt}Prob>|t| = {res}    0.7978

95%{txt} confidence set for null hypothesis expression: {res}[−.0208, .01391]
{txt}{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#3.education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:112,058}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:56}
{txt}{col 52}{lalign 17:F({res:161}, {res:111841})}{col 69} = {res}{ralign 7:19.97}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0404}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0385}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4241}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2} .0039797{col 58}{space 2} .0045552{col 69}{space 1}    0.87{col 78}{space 3}0.382{col 86}{space 4}-.0049485{col 99}{space 3} .0129078
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2} -.092331{col 58}{space 2} .1634807{col 69}{space 1}   -0.56{col 78}{space 3}0.572{col 86}{space 4}-.4127507{col 99}{space 3} .2280887
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}-.0153991{col 58}{space 2} .2286653{col 69}{space 1}   -0.07{col 78}{space 3}0.946{col 86}{space 4}-.4635797{col 99}{space 3} .4327814
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.1789938{col 58}{space 2} .1875309{col 69}{space 1}   -0.95{col 78}{space 3}0.340{col 86}{space 4}-.5465516{col 99}{space 3}  .188564
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.2207502{col 58}{space 2}  .186975{col 69}{space 1}   -1.18{col 78}{space 3}0.238{col 86}{space 4}-.5872185{col 99}{space 3}  .145718
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}-.2100809{col 58}{space 2} .2310202{col 69}{space 1}   -0.91{col 78}{space 3}0.363{col 86}{space 4} -.662877{col 99}{space 3} .2427152
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}-.1766027{col 58}{space 2} .1960233{col 69}{space 1}   -0.90{col 78}{space 3}0.368{col 86}{space 4}-.5608055{col 99}{space 3} .2076001
{txt}{space 39}1993  {c |}{col 46}{res}{space 2}-.0206428{col 58}{space 2} .1838604{col 69}{space 1}   -0.11{col 78}{space 3}0.911{col 86}{space 4}-.3810066{col 99}{space 3}  .339721
{txt}{space 39}1994  {c |}{col 46}{res}{space 2} .0155935{col 58}{space 2} .1865014{col 69}{space 1}    0.08{col 78}{space 3}0.933{col 86}{space 4}-.3499465{col 99}{space 3} .3811335
{txt}{space 39}1995  {c |}{col 46}{res}{space 2}-.0887798{col 58}{space 2} .1821956{col 69}{space 1}   -0.49{col 78}{space 3}0.626{col 86}{space 4}-.4458804{col 99}{space 3} .2683208
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}-.0512773{col 58}{space 2} .1980229{col 69}{space 1}   -0.26{col 78}{space 3}0.796{col 86}{space 4}-.4393993{col 99}{space 3} .3368446
{txt}{space 39}1997  {c |}{col 46}{res}{space 2} .6225294{col 58}{space 2} .2238564{col 69}{space 1}    2.78{col 78}{space 3}0.005{col 86}{space 4} .1837741{col 99}{space 3} 1.061285
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .3203743{col 58}{space 2} .2368606{col 69}{space 1}    1.35{col 78}{space 3}0.176{col 86}{space 4}-.1438689{col 99}{space 3} .7846175
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .3009015{col 58}{space 2} .2331923{col 69}{space 1}    1.29{col 78}{space 3}0.197{col 86}{space 4}-.1561518{col 99}{space 3} .7579549
{txt}{space 39}2000  {c |}{col 46}{res}{space 2}  .507325{col 58}{space 2} .1657985{col 69}{space 1}    3.06{col 78}{space 3}0.002{col 86}{space 4} .1823624{col 99}{space 3} .8322875
{txt}{space 39}2001  {c |}{col 46}{res}{space 2} .1898345{col 58}{space 2} .2215074{col 69}{space 1}    0.86{col 78}{space 3}0.391{col 86}{space 4}-.2443168{col 99}{space 3} .6239858
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .1715289{col 58}{space 2} .2265313{col 69}{space 1}    0.76{col 78}{space 3}0.449{col 86}{space 4} -.272469{col 99}{space 3} .6155269
{txt}{space 39}2003  {c |}{col 46}{res}{space 2} .3057403{col 58}{space 2} .2160392{col 69}{space 1}    1.42{col 78}{space 3}0.157{col 86}{space 4}-.1176934{col 99}{space 3} .7291739
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} .3897718{col 58}{space 2} .1647307{col 69}{space 1}    2.37{col 78}{space 3}0.018{col 86}{space 4} .0669022{col 99}{space 3} .7126415
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}-.3297716{col 58}{space 2} .2177281{col 69}{space 1}   -1.51{col 78}{space 3}0.130{col 86}{space 4}-.7565154{col 99}{space 3} .0969722
{txt}{space 39}2006  {c |}{col 46}{res}{space 2}-.1272619{col 58}{space 2} .2042864{col 69}{space 1}   -0.62{col 78}{space 3}0.533{col 86}{space 4}-.5276602{col 99}{space 3} .2731365
{txt}{space 39}2007  {c |}{col 46}{res}{space 2}-.0175263{col 58}{space 2} .1967152{col 69}{space 1}   -0.09{col 78}{space 3}0.929{col 86}{space 4}-.4030852{col 99}{space 3} .3680326
{txt}{space 39}2008  {c |}{col 46}{res}{space 2} .0348397{col 58}{space 2} .1639399{col 69}{space 1}    0.21{col 78}{space 3}0.832{col 86}{space 4}-.2864801{col 99}{space 3} .3561595
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.2694464{col 58}{space 2} .2039418{col 69}{space 1}   -1.32{col 78}{space 3}0.186{col 86}{space 4}-.6691692{col 99}{space 3} .1302765
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}-.3086447{col 58}{space 2} .2402879{col 69}{space 1}   -1.28{col 78}{space 3}0.199{col 86}{space 4}-.7796053{col 99}{space 3} .1623159
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.3390392{col 58}{space 2} .2590367{col 69}{space 1}   -1.31{col 78}{space 3}0.191{col 86}{space 4}-.8467472{col 99}{space 3} .1686689
{txt}{space 39}2012  {c |}{col 46}{res}{space 2}  .237428{col 58}{space 2} .2555592{col 69}{space 1}    0.93{col 78}{space 3}0.353{col 86}{space 4}-.2634641{col 99}{space 3} .7383202
{txt}{space 39}2013  {c |}{col 46}{res}{space 2} .1258527{col 58}{space 2} .2484462{col 69}{space 1}    0.51{col 78}{space 3}0.612{col 86}{space 4}-.3610982{col 99}{space 3} .6128036
{txt}{space 39}2014  {c |}{col 46}{res}{space 2} .0208999{col 58}{space 2} .2665477{col 69}{space 1}    0.08{col 78}{space 3}0.938{col 86}{space 4}-.5015297{col 99}{space 3} .5433294
{txt}{space 39}2015  {c |}{col 46}{res}{space 2} .3688332{col 58}{space 2} .2654981{col 69}{space 1}    1.39{col 78}{space 3}0.165{col 86}{space 4} -.151539{col 99}{space 3} .8892055
{txt}{space 39}2016  {c |}{col 46}{res}{space 2} .1157857{col 58}{space 2}  .243899{col 69}{space 1}    0.47{col 78}{space 3}0.635{col 86}{space 4}-.3622528{col 99}{space 3} .5938242
{txt}{space 39}2017  {c |}{col 46}{res}{space 2} .4526687{col 58}{space 2} .2527542{col 69}{space 1}    1.79{col 78}{space 3}0.073{col 86}{space 4}-.0427257{col 99}{space 3} .9480631
{txt}{space 39}2018  {c |}{col 46}{res}{space 2} .2994792{col 58}{space 2} .2543908{col 69}{space 1}    1.18{col 78}{space 3}0.239{col 86}{space 4} -.199123{col 99}{space 3} .7980815
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.1919443{col 58}{space 2} .2135395{col 69}{space 1}   -0.90{col 78}{space 3}0.369{col 86}{space 4}-.6104786{col 99}{space 3} .2265899
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}-.0390491{col 58}{space 2} .2187079{col 69}{space 1}   -0.18{col 78}{space 3}0.858{col 86}{space 4}-.4677133{col 99}{space 3} .3896151
{txt}{space 39}2021  {c |}{col 46}{res}{space 2} -.349401{col 58}{space 2} .2298203{col 69}{space 1}   -1.52{col 78}{space 3}0.128{col 86}{space 4}-.7998454{col 99}{space 3} .1010434
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.1493571{col 58}{space 2} .2048762{col 69}{space 1}   -0.73{col 78}{space 3}0.466{col 86}{space 4}-.5509114{col 99}{space 3} .2521973
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.1089395{col 58}{space 2} .0325141{col 69}{space 1}   -3.35{col 78}{space 3}0.001{col 86}{space 4}-.1726666{col 99}{space 3}-.0452123
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2}-.0239349{col 58}{space 2} .0270042{col 69}{space 1}   -0.89{col 78}{space 3}0.375{col 86}{space 4}-.0768627{col 99}{space 3}  .028993
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0302547{col 58}{space 2} .0195423{col 69}{space 1}    1.55{col 78}{space 3}0.122{col 86}{space 4}-.0080478{col 99}{space 3} .0685573
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0174447{col 58}{space 2} .0225548{col 69}{space 1}    0.77{col 78}{space 3}0.439{col 86}{space 4}-.0267624{col 99}{space 3} .0616519
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2}-.0020939{col 58}{space 2} .0234522{col 69}{space 1}   -0.09{col 78}{space 3}0.929{col 86}{space 4}-.0480597{col 99}{space 3}  .043872
{txt}{space 38}avila  {c |}{col 46}{res}{space 2}-.0179058{col 58}{space 2} .0372232{col 69}{space 1}   -0.48{col 78}{space 3}0.630{col 86}{space 4}-.0908628{col 99}{space 3} .0550512
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0577423{col 58}{space 2} .0231568{col 69}{space 1}    2.49{col 78}{space 3}0.013{col 86}{space 4} .0123552{col 99}{space 3} .1031293
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.0718454{col 58}{space 2}  .021453{col 69}{space 1}   -3.35{col 78}{space 3}0.001{col 86}{space 4}-.1138931{col 99}{space 3}-.0297978
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0305722{col 58}{space 2} .0320696{col 69}{space 1}    0.95{col 78}{space 3}0.340{col 86}{space 4}-.0322838{col 99}{space 3} .0934281
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2} .0596075{col 58}{space 2} .0282025{col 69}{space 1}    2.11{col 78}{space 3}0.035{col 86}{space 4} .0043311{col 99}{space 3}  .114884
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2}-.0206559{col 58}{space 2} .0213578{col 69}{space 1}   -0.97{col 78}{space 3}0.333{col 86}{space 4}-.0625168{col 99}{space 3}  .021205
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2}-.0049353{col 58}{space 2} .0217699{col 69}{space 1}   -0.23{col 78}{space 3}0.821{col 86}{space 4} -.047604{col 99}{space 3} .0377335
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2}-.0006093{col 58}{space 2} .0234105{col 69}{space 1}   -0.03{col 78}{space 3}0.979{col 86}{space 4}-.0464936{col 99}{space 3}  .045275
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0222325{col 58}{space 2} .0273061{col 69}{space 1}    0.81{col 78}{space 3}0.416{col 86}{space 4}-.0312869{col 99}{space 3}  .075752
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2}-.0299086{col 58}{space 2} .0218506{col 69}{space 1}   -1.37{col 78}{space 3}0.171{col 86}{space 4}-.0727354{col 99}{space 3} .0129183
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2} .0279834{col 58}{space 2} .0359048{col 69}{space 1}    0.78{col 78}{space 3}0.436{col 86}{space 4}-.0423895{col 99}{space 3} .0983563
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.1295986{col 58}{space 2} .0254512{col 69}{space 1}   -5.09{col 78}{space 3}0.000{col 86}{space 4}-.1794826{col 99}{space 3}-.0797146
{txt}{space 37}girona  {c |}{col 46}{res}{space 2}-.1138268{col 58}{space 2} .0282994{col 69}{space 1}   -4.02{col 78}{space 3}0.000{col 86}{space 4}-.1692932{col 99}{space 3}-.0583603
{txt}{space 36}granada  {c |}{col 46}{res}{space 2} .0380879{col 58}{space 2} .0217611{col 69}{space 1}    1.75{col 78}{space 3}0.080{col 86}{space 4}-.0045635{col 99}{space 3} .0807392
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0780166{col 58}{space 2} .0299308{col 69}{space 1}    2.61{col 78}{space 3}0.009{col 86}{space 4} .0193528{col 99}{space 3} .1366804
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2} .0335946{col 58}{space 2} .0293399{col 69}{space 1}    1.15{col 78}{space 3}0.252{col 86}{space 4}-.0239112{col 99}{space 3} .0911005
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2} .0049773{col 58}{space 2} .0329409{col 69}{space 1}    0.15{col 78}{space 3}0.880{col 86}{space 4}-.0595865{col 99}{space 3}  .069541
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2} -.009176{col 58}{space 2} .0272583{col 69}{space 1}   -0.34{col 78}{space 3}0.736{col 86}{space 4}-.0626018{col 99}{space 3} .0442498
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0009716{col 58}{space 2} .0233807{col 69}{space 1}    0.04{col 78}{space 3}0.967{col 86}{space 4}-.0448542{col 99}{space 3} .0467974
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0497339{col 58}{space 2} .0253275{col 69}{space 1}    1.96{col 78}{space 3}0.050{col 86}{space 4} .0000923{col 99}{space 3} .0993754
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2}-.0081933{col 58}{space 2} .0248968{col 69}{space 1}   -0.33{col 78}{space 3}0.742{col 86}{space 4}-.0569907{col 99}{space 3}  .040604
{txt}{space 39}leon  {c |}{col 46}{res}{space 2}-.0010243{col 58}{space 2} .0276603{col 69}{space 1}   -0.04{col 78}{space 3}0.970{col 86}{space 4}-.0552381{col 99}{space 3} .0531894
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2}-.0862142{col 58}{space 2} .0275005{col 69}{space 1}   -3.14{col 78}{space 3}0.002{col 86}{space 4}-.1401149{col 99}{space 3}-.0323136
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2}-.0060834{col 58}{space 2} .0263617{col 69}{space 1}   -0.23{col 78}{space 3}0.817{col 86}{space 4}-.0577521{col 99}{space 3} .0455852
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2} -.022029{col 58}{space 2} .0185145{col 69}{space 1}   -1.19{col 78}{space 3}0.234{col 86}{space 4}-.0583171{col 99}{space 3} .0142591
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0215219{col 58}{space 2} .0195694{col 69}{space 1}    1.10{col 78}{space 3}0.271{col 86}{space 4}-.0168339{col 99}{space 3} .0598777
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2}-.0064187{col 58}{space 2} .0194252{col 69}{space 1}   -0.33{col 78}{space 3}0.741{col 86}{space 4}-.0444917{col 99}{space 3} .0316543
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0400719{col 58}{space 2}   .02571{col 69}{space 1}   -1.56{col 78}{space 3}0.119{col 86}{space 4}-.0904631{col 99}{space 3} .0103192
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2}-.0041014{col 58}{space 2} .0286604{col 69}{space 1}   -0.14{col 78}{space 3}0.886{col 86}{space 4}-.0602754{col 99}{space 3} .0520725
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2}-.0061555{col 58}{space 2} .0364879{col 69}{space 1}   -0.17{col 78}{space 3}0.866{col 86}{space 4}-.0776713{col 99}{space 3} .0653603
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2}-.0249307{col 58}{space 2} .0206619{col 69}{space 1}   -1.21{col 78}{space 3}0.228{col 86}{space 4}-.0654276{col 99}{space 3} .0155663
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2} .0240954{col 58}{space 2} .0285754{col 69}{space 1}    0.84{col 78}{space 3}0.399{col 86}{space 4}-.0319119{col 99}{space 3} .0801027
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .0368552{col 58}{space 2} .0210559{col 69}{space 1}    1.75{col 78}{space 3}0.080{col 86}{space 4}-.0044141{col 99}{space 3} .0781246
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2} .0718708{col 58}{space 2}  .048704{col 69}{space 1}    1.48{col 78}{space 3}0.140{col 86}{space 4}-.0235883{col 99}{space 3}   .16733
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2}-.0176128{col 58}{space 2} .0212511{col 69}{space 1}   -0.83{col 78}{space 3}0.407{col 86}{space 4}-.0592646{col 99}{space 3}  .024039
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .1322134{col 58}{space 2}  .079719{col 69}{space 1}    1.66{col 78}{space 3}0.097{col 86}{space 4}-.0240346{col 99}{space 3} .2884613
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2} -.082309{col 58}{space 2} .0275703{col 69}{space 1}   -2.99{col 78}{space 3}0.003{col 86}{space 4}-.1363462{col 99}{space 3}-.0282717
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2}-.0424005{col 58}{space 2} .0376299{col 69}{space 1}   -1.13{col 78}{space 3}0.260{col 86}{space 4}-.1161545{col 99}{space 3} .0313535
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2} .0493133{col 58}{space 2} .0230739{col 69}{space 1}    2.14{col 78}{space 3}0.033{col 86}{space 4} .0040888{col 99}{space 3} .0945377
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2} .0111807{col 58}{space 2} .0177552{col 69}{space 1}    0.63{col 78}{space 3}0.529{col 86}{space 4}-.0236191{col 99}{space 3} .0459806
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2} -.021209{col 58}{space 2} .0230111{col 69}{space 1}   -0.92{col 78}{space 3}0.357{col 86}{space 4}-.0663105{col 99}{space 3} .0238926
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.1080984{col 58}{space 2} .0197996{col 69}{space 1}   -5.46{col 78}{space 3}0.000{col 86}{space 4}-.1469053{col 99}{space 3}-.0692915
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2} .0582031{col 58}{space 2}  .037648{col 69}{space 1}    1.55{col 78}{space 3}0.122{col 86}{space 4}-.0155865{col 99}{space 3} .1319927
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2}-.0046841{col 58}{space 2} .0218409{col 69}{space 1}   -0.21{col 78}{space 3}0.830{col 86}{space 4}-.0474919{col 99}{space 3} .0381237
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2}-.0105104{col 58}{space 2} .0103459{col 69}{space 1}   -1.02{col 78}{space 3}0.310{col 86}{space 4}-.0307883{col 99}{space 3} .0097675
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2}-.0055346{col 58}{space 2} .0099502{col 69}{space 1}   -0.56{col 78}{space 3}0.578{col 86}{space 4}-.0250368{col 99}{space 3} .0139677
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0045685{col 58}{space 2} .0110149{col 69}{space 1}    0.41{col 78}{space 3}0.678{col 86}{space 4}-.0170205{col 99}{space 3} .0261574
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0104424{col 58}{space 2}  .010142{col 69}{space 1}    1.03{col 78}{space 3}0.303{col 86}{space 4}-.0094357{col 99}{space 3} .0303205
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2} -.017147{col 58}{space 2} .0138301{col 69}{space 1}   -1.24{col 78}{space 3}0.215{col 86}{space 4}-.0442537{col 99}{space 3} .0099598
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2} .0188937{col 58}{space 2} .0132514{col 69}{space 1}    1.43{col 78}{space 3}0.154{col 86}{space 4}-.0070789{col 99}{space 3} .0448663
{txt}{space 44} {c |}
{space 41}age {c |}{col 46}{res}{space 2}  .001583{col 58}{space 2} .0002018{col 69}{space 1}    7.85{col 78}{space 3}0.000{col 86}{space 4} .0011875{col 99}{space 3} .0019784
{txt}{space 44} {c |}
{space 35}education {c |}
{space 34}Secondary  {c |}{col 46}{res}{space 2}-.0242141{col 58}{space 2} .0058089{col 69}{space 1}   -4.17{col 78}{space 3}0.000{col 86}{space 4}-.0355995{col 99}{space 3}-.0128287
{txt}{space 27}Higher Education  {c |}{col 46}{res}{space 2}-.0353552{col 58}{space 2} .0061577{col 69}{space 1}   -5.74{col 78}{space 3}0.000{col 86}{space 4}-.0474242{col 99}{space 3}-.0232862
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0074844{col 58}{space 2} .0065328{col 69}{space 1}    1.15{col 78}{space 3}0.252{col 86}{space 4}-.0053199{col 99}{space 3} .0202886
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0334407{col 58}{space 2} .0079444{col 69}{space 1}    4.21{col 78}{space 3}0.000{col 86}{space 4} .0178698{col 99}{space 3} .0490117
{txt}{space 36}Student  {c |}{col 46}{res}{space 2} .0311699{col 58}{space 2} .0119871{col 69}{space 1}    2.60{col 78}{space 3}0.009{col 86}{space 4} .0076755{col 99}{space 3} .0546644
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2} .0210002{col 58}{space 2} .0083208{col 69}{space 1}    2.52{col 78}{space 3}0.012{col 86}{space 4} .0046916{col 99}{space 3} .0373088
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2} .0506978{col 58}{space 2} .0162673{col 69}{space 1}    3.12{col 78}{space 3}0.002{col 86}{space 4} .0188141{col 99}{space 3} .0825816
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0550947{col 58}{space 2} .0141289{col 69}{space 1}    3.90{col 78}{space 3}0.000{col 86}{space 4} .0274023{col 99}{space 3} .0827871
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0495887{col 58}{space 2} .0132991{col 69}{space 1}    3.73{col 78}{space 3}0.000{col 86}{space 4} .0235226{col 99}{space 3} .0756548
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0277032{col 58}{space 2} .0142806{col 69}{space 1}    1.94{col 78}{space 3}0.052{col 86}{space 4}-.0002865{col 99}{space 3} .0556929
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0424861{col 58}{space 2} .0130491{col 69}{space 1}    3.26{col 78}{space 3}0.001{col 86}{space 4} .0169101{col 99}{space 3} .0680621
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2} .0013298{col 58}{space 2} .0185233{col 69}{space 1}    0.07{col 78}{space 3}0.943{col 86}{space 4}-.0349755{col 99}{space 3} .0376352
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2}-.0008277{col 58}{space 2}   .00024{col 69}{space 1}   -3.45{col 78}{space 3}0.001{col 86}{space 4}-.0012982{col 99}{space 3}-.0003573
{txt}{space 44} {c |}
{space 25}preperiod#education {c |}
{space 19}1#Primary School or less  {c |}{col 46}{res}{space 2}-.0245001{col 58}{space 2} .0082186{col 69}{space 1}   -2.98{col 78}{space 3}0.003{col 86}{space 4}-.0406085{col 99}{space 3}-.0083917
{txt}{space 32}1#Secondary  {c |}{col 46}{res}{space 2}-.0053113{col 58}{space 2} .0080741{col 69}{space 1}   -0.66{col 78}{space 3}0.511{col 86}{space 4}-.0211365{col 99}{space 3} .0105139
{txt}{space 25}1#Higher Education  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2}  .005007{col 58}{space 2} .0094862{col 69}{space 1}    0.53{col 78}{space 3}0.598{col 86}{space 4}-.0135858{col 99}{space 3} .0235998
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2}-.0103549{col 58}{space 2} .0109975{col 69}{space 1}   -0.94{col 78}{space 3}0.346{col 86}{space 4}-.0319099{col 99}{space 3}    .0112
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2} -.009241{col 58}{space 2} .0096603{col 69}{space 1}   -0.96{col 78}{space 3}0.339{col 86}{space 4} -.028175{col 99}{space 3}  .009693
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2} -.060498{col 58}{space 2} .0165864{col 69}{space 1}   -3.65{col 78}{space 3}0.000{col 86}{space 4}-.0930071{col 99}{space 3}-.0279889
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2}-.0130984{col 58}{space 2} .0270506{col 69}{space 1}   -0.48{col 78}{space 3}0.628{col 86}{space 4}-.0661171{col 99}{space 3} .0399204
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2}-.0157477{col 58}{space 2} .0385144{col 69}{space 1}   -0.41{col 78}{space 3}0.683{col 86}{space 4}-.0912354{col 99}{space 3}   .05974
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .0673718{col 58}{space 2} .0346754{col 69}{space 1}    1.94{col 78}{space 3}0.052{col 86}{space 4}-.0005915{col 99}{space 3} .1353352
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0591259{col 58}{space 2} .0273868{col 69}{space 1}    2.16{col 78}{space 3}0.031{col 86}{space 4} .0054481{col 99}{space 3} .1128037
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2}  .037487{col 58}{space 2} .0308463{col 69}{space 1}    1.22{col 78}{space 3}0.224{col 86}{space 4}-.0229712{col 99}{space 3} .0979452
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2}-.0068282{col 58}{space 2} .0308377{col 69}{space 1}   -0.22{col 78}{space 3}0.825{col 86}{space 4}-.0672697{col 99}{space 3} .0536133
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0526605{col 58}{space 2} .0449998{col 69}{space 1}    1.17{col 78}{space 3}0.242{col 86}{space 4}-.0355384{col 99}{space 3} .1408594
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0359908{col 58}{space 2} .0306813{col 69}{space 1}    1.17{col 78}{space 3}0.241{col 86}{space 4}-.0241441{col 99}{space 3} .0961257
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} .0478524{col 58}{space 2} .0265745{col 69}{space 1}    1.80{col 78}{space 3}0.072{col 86}{space 4}-.0042332{col 99}{space 3}  .099938
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2} -.007862{col 58}{space 2} .0394199{col 69}{space 1}   -0.20{col 78}{space 3}0.842{col 86}{space 4}-.0851243{col 99}{space 3} .0694004
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2}-.0016077{col 58}{space 2} .0354804{col 69}{space 1}   -0.05{col 78}{space 3}0.964{col 86}{space 4}-.0711487{col 99}{space 3} .0679333
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .0492676{col 58}{space 2} .0294115{col 69}{space 1}    1.68{col 78}{space 3}0.094{col 86}{space 4}-.0083786{col 99}{space 3} .1069138
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .0805124{col 58}{space 2}  .030142{col 69}{space 1}    2.67{col 78}{space 3}0.008{col 86}{space 4} .0214345{col 99}{space 3} .1395904
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .0649326{col 58}{space 2} .0309613{col 69}{space 1}    2.10{col 78}{space 3}0.036{col 86}{space 4} .0042488{col 99}{space 3} .1256164
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2} .0407888{col 58}{space 2} .0348437{col 69}{space 1}    1.17{col 78}{space 3}0.242{col 86}{space 4}-.0275043{col 99}{space 3}  .109082
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2}-.0061092{col 58}{space 2} .0293856{col 69}{space 1}   -0.21{col 78}{space 3}0.835{col 86}{space 4}-.0637045{col 99}{space 3} .0514861
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2} .0122982{col 58}{space 2} .0434013{col 69}{space 1}    0.28{col 78}{space 3}0.777{col 86}{space 4}-.0727677{col 99}{space 3} .0973641
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2} -.060808{col 58}{space 2} .0314214{col 69}{space 1}   -1.94{col 78}{space 3}0.053{col 86}{space 4}-.1223934{col 99}{space 3} .0007775
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2} .0284035{col 58}{space 2} .0344147{col 69}{space 1}    0.83{col 78}{space 3}0.409{col 86}{space 4}-.0390488{col 99}{space 3} .0958559
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2}-.0170516{col 58}{space 2} .0297434{col 69}{space 1}   -0.57{col 78}{space 3}0.566{col 86}{space 4}-.0753482{col 99}{space 3}  .041245
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2}-.0735413{col 58}{space 2} .0380917{col 69}{space 1}   -1.93{col 78}{space 3}0.054{col 86}{space 4}-.1482005{col 99}{space 3} .0011179
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} 2.50e-06{col 58}{space 2} .0365842{col 69}{space 1}    0.00{col 78}{space 3}1.000{col 86}{space 4} -.071702{col 99}{space 3}  .071707
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2}-.0290372{col 58}{space 2}  .040607{col 69}{space 1}   -0.72{col 78}{space 3}0.475{col 86}{space 4}-.1086264{col 99}{space 3}  .050552
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .0529819{col 58}{space 2} .0325245{col 69}{space 1}    1.63{col 78}{space 3}0.103{col 86}{space 4}-.0107657{col 99}{space 3} .1167294
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2}  .019362{col 58}{space 2} .0310619{col 69}{space 1}    0.62{col 78}{space 3}0.533{col 86}{space 4}-.0415189{col 99}{space 3} .0802429
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0834302{col 58}{space 2} .0341807{col 69}{space 1}   -2.44{col 78}{space 3}0.015{col 86}{space 4} -.150424{col 99}{space 3}-.0164365
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .0613179{col 58}{space 2} .0317405{col 69}{space 1}    1.93{col 78}{space 3}0.053{col 86}{space 4}-.0008931{col 99}{space 3} .1235289
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2} .0049814{col 58}{space 2} .0350779{col 69}{space 1}    0.14{col 78}{space 3}0.887{col 86}{space 4}-.0637708{col 99}{space 3} .0737337
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2}-.0065945{col 58}{space 2} .0361479{col 69}{space 1}   -0.18{col 78}{space 3}0.855{col 86}{space 4}-.0774439{col 99}{space 3} .0642549
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0247888{col 58}{space 2} .0341093{col 69}{space 1}    0.73{col 78}{space 3}0.467{col 86}{space 4}-.0420649{col 99}{space 3} .0916425
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0417355{col 58}{space 2} .0248014{col 69}{space 1}    1.68{col 78}{space 3}0.092{col 86}{space 4}-.0068749{col 99}{space 3}  .090346
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .0598102{col 58}{space 2} .0249819{col 69}{space 1}    2.39{col 78}{space 3}0.017{col 86}{space 4}  .010846{col 99}{space 3} .1087744
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2} .0258594{col 58}{space 2} .0259688{col 69}{space 1}    1.00{col 78}{space 3}0.319{col 86}{space 4}-.0250392{col 99}{space 3}  .076758
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2}-.0346801{col 58}{space 2} .0324811{col 69}{space 1}   -1.07{col 78}{space 3}0.286{col 86}{space 4}-.0983425{col 99}{space 3} .0289823
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2} .0321537{col 58}{space 2} .0352758{col 69}{space 1}    0.91{col 78}{space 3}0.362{col 86}{space 4}-.0369863{col 99}{space 3} .1012937
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2}  .012591{col 58}{space 2} .0445617{col 69}{space 1}    0.28{col 78}{space 3}0.778{col 86}{space 4}-.0747494{col 99}{space 3} .0999313
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}-.0131537{col 58}{space 2} .0278767{col 69}{space 1}   -0.47{col 78}{space 3}0.637{col 86}{space 4}-.0677916{col 99}{space 3} .0414841
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .0751804{col 58}{space 2} .0361458{col 69}{space 1}    2.08{col 78}{space 3}0.038{col 86}{space 4} .0043351{col 99}{space 3} .1460256
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0319228{col 58}{space 2} .0288235{col 69}{space 1}   -1.11{col 78}{space 3}0.268{col 86}{space 4}-.0884165{col 99}{space 3} .0245709
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0642052{col 58}{space 2} .0556628{col 69}{space 1}   -1.15{col 78}{space 3}0.249{col 86}{space 4}-.1733035{col 99}{space 3} .0448932
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .1041175{col 58}{space 2} .0259945{col 69}{space 1}    4.01{col 78}{space 3}0.000{col 86}{space 4} .0531687{col 99}{space 3} .1550663
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2}-.0890577{col 58}{space 2} .0859812{col 69}{space 1}   -1.04{col 78}{space 3}0.300{col 86}{space 4}-.2575796{col 99}{space 3} .0794642
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2} .0463423{col 58}{space 2}  .033817{col 69}{space 1}    1.37{col 78}{space 3}0.171{col 86}{space 4}-.0199384{col 99}{space 3} .1126231
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0284075{col 58}{space 2} .0443086{col 69}{space 1}    0.64{col 78}{space 3}0.521{col 86}{space 4}-.0584367{col 99}{space 3} .1152518
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2}-.0098668{col 58}{space 2} .0313607{col 69}{space 1}   -0.31{col 78}{space 3}0.753{col 86}{space 4}-.0713332{col 99}{space 3} .0515996
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2} .0417398{col 58}{space 2} .0234428{col 69}{space 1}    1.78{col 78}{space 3}0.075{col 86}{space 4}-.0042078{col 99}{space 3} .0876874
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2} .0604048{col 58}{space 2}  .031232{col 69}{space 1}    1.93{col 78}{space 3}0.053{col 86}{space 4}-.0008094{col 99}{space 3}  .121619
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0813167{col 58}{space 2} .0268634{col 69}{space 1}   -3.03{col 78}{space 3}0.002{col 86}{space 4}-.1339685{col 99}{space 3}-.0286648
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2} .0232023{col 58}{space 2} .0451035{col 69}{space 1}    0.51{col 78}{space 3}0.607{col 86}{space 4}-.0651999{col 99}{space 3} .1116045
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1916845{col 58}{space 2} .0304358{col 69}{space 1}    6.30{col 78}{space 3}0.000{col 86}{space 4} .1320309{col 99}{space 3} .2513382
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}111841{txt}) = {res}8.788{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.5245
{col 25}{txt}Prob>|t| = {res}    0.6126

95%{txt} confidence set for null hypothesis expression: {res}[−.0137, .02166]
{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_20_21_female.tex"'})

{com}. 
. 
. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLES 22 & 23: Effects of Last Year's Lottery Prizes on Survey 
. *** Measures of Incumbent Party Support for Respondents Below and Above the 
. *** Median Age (45 y/o)
. **----------------------------------------------------------------------------**
. 
. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. ** Period control:
. gen preperiod=1 if year_original<2010
{txt}(448,934 missing values generated)

{com}. replace preperiod=0 if year_original>=2010
{txt}(448,934 real changes made)

{com}. 
. global individual_characteristics "i.municipality_size female age i.education i.status"
{txt}
{com}. 
. global ind_char_preperiod "preperiod#i.municipality_size preperiod#female preperiod#c.age preperiod#i.education preperiod#i.status"
{txt}
{com}. 
. 
. * Loop over age<45 (Below median age) and age>=45 (At or above median age)
. 
. foreach g in young old {c -(}
{txt}  2{com}.     
.     preserve
{txt}  3{com}.         if "`g'" == "young" {c -(}
{txt}  4{com}.             keep if age < 45 & month<4
{txt}  5{com}.         {c )-}
{txt}  6{com}.         else if "`g'" == "old" {c -(}
{txt}  7{com}.             keep if age >= 45 & month<4
{txt}  8{com}.         {c )-}
{txt}  9{com}.         
.                 ** Col 4, Appendix Tables 22 & 23: 
.                 eststo b_preQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num $individual_characteristics if year_original<2010, absorb(survey) 
{txt} 10{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 11{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 12{com}.                 estadd local YearFE "$\checkmark$"
{txt} 13{com}.                 estadd local PeriodFE "$\times$"
{txt} 14{com}.                 estadd local PeriodCtrols "$\times$"
{txt} 15{com}.                 estadd local Data "Ours"
{txt} 16{com}.                 estadd local Sample "Pre-10"
{txt} 17{com}.                 estadd local Propensity "$\checkmark$"
{txt} 18{com}.                 estadd local Estimation "OLS"
{txt} 19{com}.                 estadd local Outcome "Q1"
{txt} 20{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 21{com}.                 mean(vote_incumbent) if e(sample)==1
{txt} 22{com}. 
.                 ** Col 5, Appendix Tables 22 & 23: 
.                 eststo b_postQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num $individual_characteristics if year_original>=2010, absorb(survey) 
{txt} 23{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 24{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 25{com}.                 estadd local YearFE "$\checkmark$"
{txt} 26{com}.                 estadd local PeriodFE "$\times$"
{txt} 27{com}.                 estadd local PeriodCtrols "$\times$"
{txt} 28{com}.                 estadd local Data "Ours"
{txt} 29{com}.                 estadd local Sample "Post-09"
{txt} 30{com}.                 estadd local Propensity "$\checkmark$"
{txt} 31{com}.                 estadd local Estimation "OLS"
{txt} 32{com}.                 estadd local Outcome "Q1"
{txt} 33{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 34{com}.                 mean(vote_incumbent) if e(sample)==1
{txt} 35{com}. 
.                 ** Col 6, Appendix Tables 22 & 23: 
.                 eststo b_pooledQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num preperiod $individual_characteristics $ind_char_preperiod i.prov_num#preperiod, absorb(survey) 
{txt} 36{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 37{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 38{com}.                 estadd local YearFE "$\checkmark$"
{txt} 39{com}.                 estadd local PeriodFE "$\checkmark$"
{txt} 40{com}.                 estadd local PeriodCtrols "$\checkmark$"
{txt} 41{com}.                 estadd local Data "Ours"
{txt} 42{com}.                 estadd local Sample "Pooled"
{txt} 43{com}.                 estadd local Propensity "$\checkmark$"
{txt} 44{com}.                 estadd local Estimation "OLS"
{txt} 45{com}.                 estadd local Outcome "Q1"
{txt} 46{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 47{com}.                 mean(vote_incumbent) if e(sample)==1
{txt} 48{com}. 
.                 
.                 *** Appendix Tables 22, 23: Assemble tables 
.                 
.                 esttab b_preQ1 b_postQ1 b_pooledQ1 using ${c -(}tables{c )-}Survey_Appendix_Tables_22_23_`g'.tex, ///
>                         keep(top_prizes_gdp_ours) nocon r2 nostar ///           
>                         cells(b(fmt(3)) se(fmt(3) par) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>                         mtitles se mtitles("Vote Incumb." "Vote Incumb." ///
>                         "Vote Incumb.")  ///
>                         coeflabels(top_prizes_gdp_ours "Top Lottery prizes" expenditure_gdp_ours "Lottery expenditure") ///
>                         scalars("Estimation Estimation" "Data Data" "Sample Sample" "Outcome Time Outcome" "ProvinceFE Province FE" "YearFE Year FE" "SurveyFE Survey FE" "Propensity Propensity corrected")  replace                           
{txt} 49{com}.                         
.                                 
.                 /* NOTE: The WCB confidence sets are manually added to the tables from the Stata output generated here. */
. 
.     restore
{txt} 50{com}. {c )-}
{txt}(961,414 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:74,098}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:87}, {res:73976})}{col 70} = {res}{ralign 6:24.28}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0395}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0379}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4236}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}   .00455{col 56}{space 2} .0058218{col 67}{space 1}    0.78{col 76}{space 3}0.434{col 84}{space 4}-.0068608{col 97}{space 3} .0159608
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.1367681{col 56}{space 2} .1578889{col 67}{space 1}   -0.87{col 76}{space 3}0.386{col 84}{space 4}-.4462297{col 97}{space 3} .1726935
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2} .0540722{col 56}{space 2} .2205719{col 67}{space 1}    0.25{col 76}{space 3}0.806{col 84}{space 4}-.3782479{col 97}{space 3} .4863922
{txt}{space 37}1989  {c |}{col 44}{res}{space 2} -.119769{col 56}{space 2} .1815319{col 67}{space 1}   -0.66{col 76}{space 3}0.509{col 84}{space 4}-.4755707{col 97}{space 3} .2360327
{txt}{space 37}1990  {c |}{col 44}{res}{space 2} -.240246{col 56}{space 2} .1806932{col 67}{space 1}   -1.33{col 76}{space 3}0.184{col 84}{space 4}-.5944041{col 97}{space 3}  .113912
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.4548789{col 56}{space 2} .2250512{col 67}{space 1}   -2.02{col 76}{space 3}0.043{col 84}{space 4}-.8959784{col 97}{space 3}-.0137794
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.1751184{col 56}{space 2} .1898413{col 67}{space 1}   -0.92{col 76}{space 3}0.356{col 84}{space 4}-.5472065{col 97}{space 3} .1969698
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}-.0816232{col 56}{space 2} .1777606{col 67}{space 1}   -0.46{col 76}{space 3}0.646{col 84}{space 4}-.4300333{col 97}{space 3} .2667868
{txt}{space 37}1994  {c |}{col 44}{res}{space 2} .0619356{col 56}{space 2} .1796143{col 67}{space 1}    0.34{col 76}{space 3}0.730{col 84}{space 4}-.2901076{col 97}{space 3} .4139789
{txt}{space 37}1995  {c |}{col 44}{res}{space 2} .0050383{col 56}{space 2} .1760924{col 67}{space 1}    0.03{col 76}{space 3}0.977{col 84}{space 4}-.3401021{col 97}{space 3} .3501786
{txt}{space 37}1996  {c |}{col 44}{res}{space 2}-.0370459{col 56}{space 2} .1921464{col 67}{space 1}   -0.19{col 76}{space 3}0.847{col 84}{space 4}-.4136521{col 97}{space 3} .3395603
{txt}{space 37}1997  {c |}{col 44}{res}{space 2}  .345802{col 56}{space 2} .2177922{col 67}{space 1}    1.59{col 76}{space 3}0.112{col 84}{space 4}-.0810699{col 97}{space 3} .7726739
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .6163924{col 56}{space 2} .2277126{col 67}{space 1}    2.71{col 76}{space 3}0.007{col 84}{space 4} .1700766{col 97}{space 3} 1.062708
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .6589289{col 56}{space 2} .2252074{col 67}{space 1}    2.93{col 76}{space 3}0.003{col 84}{space 4} .2175232{col 97}{space 3} 1.100335
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .4214428{col 56}{space 2} .1601724{col 67}{space 1}    2.63{col 76}{space 3}0.009{col 84}{space 4} .1075056{col 97}{space 3} .7353799
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} .0497658{col 56}{space 2} .2169877{col 67}{space 1}    0.23{col 76}{space 3}0.819{col 84}{space 4}-.3755292{col 97}{space 3} .4750609
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} .4237526{col 56}{space 2}  .223251{col 67}{space 1}    1.90{col 76}{space 3}0.058{col 84}{space 4}-.0138184{col 97}{space 3} .8613237
{txt}{space 37}2003  {c |}{col 44}{res}{space 2}-.0549996{col 56}{space 2} .2156793{col 67}{space 1}   -0.26{col 76}{space 3}0.799{col 84}{space 4}-.4777302{col 97}{space 3} .3677309
{txt}{space 37}2004  {c |}{col 44}{res}{space 2}  .325141{col 56}{space 2} .1590827{col 67}{space 1}    2.04{col 76}{space 3}0.041{col 84}{space 4} .0133396{col 97}{space 3} .6369425
{txt}{space 37}2005  {c |}{col 44}{res}{space 2}-.2768956{col 56}{space 2} .2107869{col 67}{space 1}   -1.31{col 76}{space 3}0.189{col 84}{space 4} -.690037{col 97}{space 3} .1362459
{txt}{space 37}2006  {c |}{col 44}{res}{space 2} .0007538{col 56}{space 2} .1987213{col 67}{space 1}    0.00{col 76}{space 3}0.997{col 84}{space 4}-.3887391{col 97}{space 3} .3902467
{txt}{space 37}2007  {c |}{col 44}{res}{space 2} .2218048{col 56}{space 2} .1906322{col 67}{space 1}    1.16{col 76}{space 3}0.245{col 84}{space 4}-.1518335{col 97}{space 3} .5954431
{txt}{space 37}2008  {c |}{col 44}{res}{space 2} .1447774{col 56}{space 2} .1583037{col 67}{space 1}    0.91{col 76}{space 3}0.360{col 84}{space 4}-.1654972{col 97}{space 3}  .455052
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}-.2592102{col 56}{space 2}  .197607{col 67}{space 1}   -1.31{col 76}{space 3}0.190{col 84}{space 4}-.6465191{col 97}{space 3} .1280987
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1360306{col 56}{space 2} .0175977{col 67}{space 1}   -7.73{col 76}{space 3}0.000{col 84}{space 4}-.1705219{col 97}{space 3}-.1015392
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0536736{col 56}{space 2} .0174737{col 67}{space 1}    3.07{col 76}{space 3}0.002{col 84}{space 4} .0194253{col 97}{space 3} .0879219
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0740847{col 56}{space 2} .0137816{col 67}{space 1}    5.38{col 76}{space 3}0.000{col 84}{space 4} .0470728{col 97}{space 3} .1010967
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0515826{col 56}{space 2} .0161099{col 67}{space 1}    3.20{col 76}{space 3}0.001{col 84}{space 4} .0200072{col 97}{space 3} .0831579
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0132385{col 56}{space 2}  .014237{col 67}{space 1}    0.93{col 76}{space 3}0.352{col 84}{space 4} -.014666{col 97}{space 3}  .041143
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0262411{col 56}{space 2} .0218631{col 67}{space 1}    1.20{col 76}{space 3}0.230{col 84}{space 4}-.0166105{col 97}{space 3} .0690928
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0767273{col 56}{space 2} .0163061{col 67}{space 1}    4.71{col 76}{space 3}0.000{col 84}{space 4} .0447675{col 97}{space 3} .1086871
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0229788{col 56}{space 2} .0116852{col 67}{space 1}   -1.97{col 76}{space 3}0.049{col 84}{space 4}-.0458818{col 97}{space 3}-.0000758
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0011591{col 56}{space 2} .0180366{col 67}{space 1}    0.06{col 76}{space 3}0.949{col 84}{space 4}-.0341927{col 97}{space 3} .0365108
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0648578{col 56}{space 2}  .017166{col 67}{space 1}    3.78{col 76}{space 3}0.000{col 84}{space 4} .0312126{col 97}{space 3}  .098503
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0628753{col 56}{space 2} .0157558{col 67}{space 1}    3.99{col 76}{space 3}0.000{col 84}{space 4} .0319941{col 97}{space 3} .0937566
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0428638{col 56}{space 2} .0160885{col 67}{space 1}    2.66{col 76}{space 3}0.008{col 84}{space 4} .0113305{col 97}{space 3} .0743971
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0596397{col 56}{space 2} .0162232{col 67}{space 1}    3.68{col 76}{space 3}0.000{col 84}{space 4} .0278424{col 97}{space 3}  .091437
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0737039{col 56}{space 2} .0173623{col 67}{space 1}    4.25{col 76}{space 3}0.000{col 84}{space 4} .0396738{col 97}{space 3}  .107734
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0385791{col 56}{space 2} .0147399{col 67}{space 1}   -2.62{col 76}{space 3}0.009{col 84}{space 4}-.0674692{col 97}{space 3}-.0096891
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0367561{col 56}{space 2} .0209757{col 67}{space 1}    1.75{col 76}{space 3}0.080{col 84}{space 4}-.0043561{col 97}{space 3} .0778684
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1848197{col 56}{space 2} .0148767{col 67}{space 1}  -12.42{col 76}{space 3}0.000{col 84}{space 4} -.213978{col 97}{space 3}-.1556615
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0880286{col 56}{space 2}   .01693{col 67}{space 1}   -5.20{col 76}{space 3}0.000{col 84}{space 4}-.1212113{col 97}{space 3}-.0548458
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0152465{col 56}{space 2} .0149641{col 67}{space 1}    1.02{col 76}{space 3}0.308{col 84}{space 4}-.0140831{col 97}{space 3} .0445762
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0286052{col 56}{space 2}  .020097{col 67}{space 1}    1.42{col 76}{space 3}0.155{col 84}{space 4}-.0107849{col 97}{space 3} .0679953
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2}  .025161{col 56}{space 2} .0185229{col 67}{space 1}    1.36{col 76}{space 3}0.174{col 84}{space 4}-.0111439{col 97}{space 3} .0614658
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0106817{col 56}{space 2} .0200465{col 67}{space 1}   -0.53{col 76}{space 3}0.594{col 84}{space 4}-.0499727{col 97}{space 3} .0286092
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0464669{col 56}{space 2} .0161046{col 67}{space 1}    2.89{col 76}{space 3}0.004{col 84}{space 4} .0149019{col 97}{space 3} .0780319
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0234318{col 56}{space 2} .0152758{col 67}{space 1}    1.53{col 76}{space 3}0.125{col 84}{space 4}-.0065087{col 97}{space 3} .0533723
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2}-.0332736{col 56}{space 2} .0184315{col 67}{space 1}   -1.81{col 76}{space 3}0.071{col 84}{space 4}-.0693992{col 97}{space 3}  .002852
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0523915{col 56}{space 2} .0155748{col 67}{space 1}    3.36{col 76}{space 3}0.001{col 84}{space 4} .0218649{col 97}{space 3}  .082918
{txt}{space 37}leon  {c |}{col 44}{res}{space 2}  .028556{col 56}{space 2} .0169909{col 67}{space 1}    1.68{col 76}{space 3}0.093{col 84}{space 4} -.004746{col 97}{space 3} .0618581
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0872887{col 56}{space 2} .0197142{col 67}{space 1}   -4.43{col 76}{space 3}0.000{col 84}{space 4}-.1259284{col 97}{space 3} -.048649
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0055102{col 56}{space 2} .0180663{col 67}{space 1}    0.31{col 76}{space 3}0.760{col 84}{space 4}-.0298996{col 97}{space 3}   .04092
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2}-.0040313{col 56}{space 2} .0121183{col 67}{space 1}   -0.33{col 76}{space 3}0.739{col 84}{space 4}-.0277832{col 97}{space 3} .0197206
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0918583{col 56}{space 2} .0141611{col 67}{space 1}    6.49{col 76}{space 3}0.000{col 84}{space 4} .0641025{col 97}{space 3}  .119614
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0217389{col 56}{space 2} .0139698{col 67}{space 1}    1.56{col 76}{space 3}0.120{col 84}{space 4}-.0056419{col 97}{space 3} .0491197
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0919118{col 56}{space 2} .0166437{col 67}{space 1}   -5.52{col 76}{space 3}0.000{col 84}{space 4}-.1245333{col 97}{space 3}-.0592903
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2}-.0136492{col 56}{space 2} .0180175{col 67}{space 1}   -0.76{col 76}{space 3}0.449{col 84}{space 4}-.0489634{col 97}{space 3} .0216651
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0255267{col 56}{space 2} .0219602{col 67}{space 1}    1.16{col 76}{space 3}0.245{col 84}{space 4}-.0175152{col 97}{space 3} .0685685
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0244307{col 56}{space 2} .0144722{col 67}{space 1}   -1.69{col 76}{space 3}0.091{col 84}{space 4}-.0527962{col 97}{space 3} .0039349
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0782029{col 56}{space 2} .0177169{col 67}{space 1}    4.41{col 76}{space 3}0.000{col 84}{space 4} .0434779{col 97}{space 3}  .112928
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0251683{col 56}{space 2} .0145982{col 67}{space 1}    1.72{col 76}{space 3}0.085{col 84}{space 4} -.003444{col 97}{space 3} .0537807
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2}-.0154593{col 56}{space 2} .0220567{col 67}{space 1}   -0.70{col 76}{space 3}0.483{col 84}{space 4}-.0586905{col 97}{space 3} .0277718
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0690106{col 56}{space 2} .0142176{col 67}{space 1}    4.85{col 76}{space 3}0.000{col 84}{space 4} .0411441{col 97}{space 3} .0968771
{txt}{space 36}soria  {c |}{col 44}{res}{space 2}  .055854{col 56}{space 2}  .026973{col 67}{space 1}    2.07{col 76}{space 3}0.038{col 84}{space 4} .0029869{col 97}{space 3}  .108721
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0356731{col 56}{space 2} .0166427{col 67}{space 1}   -2.14{col 76}{space 3}0.032{col 84}{space 4}-.0682926{col 97}{space 3}-.0030535
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0057746{col 56}{space 2} .0201231{col 67}{space 1}   -0.29{col 76}{space 3}0.774{col 84}{space 4}-.0452158{col 97}{space 3} .0336666
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0475937{col 56}{space 2}  .017087{col 67}{space 1}    2.79{col 76}{space 3}0.005{col 84}{space 4} .0141032{col 97}{space 3} .0810842
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0407708{col 56}{space 2} .0132321{col 67}{space 1}    3.08{col 76}{space 3}0.002{col 84}{space 4} .0148359{col 97}{space 3} .0667058
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}  .026334{col 56}{space 2} .0171221{col 67}{space 1}    1.54{col 76}{space 3}0.124{col 84}{space 4}-.0072251{col 97}{space 3} .0598932
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1702895{col 56}{space 2} .0135346{col 67}{space 1}  -12.58{col 76}{space 3}0.000{col 84}{space 4}-.1968172{col 97}{space 3}-.1437618
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0929917{col 56}{space 2} .0214288{col 67}{space 1}    4.34{col 76}{space 3}0.000{col 84}{space 4} .0509914{col 97}{space 3}  .134992
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0023899{col 56}{space 2} .0165452{col 67}{space 1}    0.14{col 76}{space 3}0.885{col 84}{space 4}-.0300387{col 97}{space 3} .0348185
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0028094{col 56}{space 2} .0068757{col 67}{space 1}   -0.41{col 76}{space 3}0.683{col 84}{space 4}-.0162857{col 97}{space 3} .0106669
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0061884{col 56}{space 2} .0067267{col 67}{space 1}   -0.92{col 76}{space 3}0.358{col 84}{space 4}-.0193728{col 97}{space 3}  .006996
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0179127{col 56}{space 2} .0077571{col 67}{space 1}   -2.31{col 76}{space 3}0.021{col 84}{space 4}-.0331167{col 97}{space 3}-.0027087
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0007275{col 56}{space 2} .0068765{col 67}{space 1}    0.11{col 76}{space 3}0.916{col 84}{space 4}-.0127505{col 97}{space 3} .0142054
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0577501{col 56}{space 2} .0103274{col 67}{space 1}   -5.59{col 76}{space 3}0.000{col 84}{space 4}-.0779917{col 97}{space 3}-.0375085
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0107695{col 56}{space 2} .0096728{col 67}{space 1}   -1.11{col 76}{space 3}0.266{col 84}{space 4} -.029728{col 97}{space 3} .0081891
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0075383{col 56}{space 2} .0034206{col 67}{space 1}   -2.20{col 76}{space 3}0.028{col 84}{space 4}-.0142427{col 97}{space 3} -.000834
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0007862{col 56}{space 2} .0002373{col 67}{space 1}    3.31{col 76}{space 3}0.001{col 84}{space 4} .0003212{col 97}{space 3} .0012513
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0224417{col 56}{space 2} .0037922{col 67}{space 1}   -5.92{col 76}{space 3}0.000{col 84}{space 4}-.0298744{col 97}{space 3}-.0150089
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0217164{col 56}{space 2} .0046201{col 67}{space 1}   -4.70{col 76}{space 3}0.000{col 84}{space 4}-.0307719{col 97}{space 3} -.012661
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0120986{col 56}{space 2}  .004669{col 67}{space 1}   -2.59{col 76}{space 3}0.010{col 84}{space 4}-.0212497{col 97}{space 3}-.0029474
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0491107{col 56}{space 2}  .016743{col 67}{space 1}    2.93{col 76}{space 3}0.003{col 84}{space 4} .0162946{col 97}{space 3} .0819268
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0233884{col 56}{space 2} .0056304{col 67}{space 1}   -4.15{col 76}{space 3}0.000{col 84}{space 4}-.0344239{col 97}{space 3}-.0123528
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0197752{col 56}{space 2} .0054988{col 67}{space 1}    3.60{col 76}{space 3}0.000{col 84}{space 4} .0089975{col 97}{space 3} .0305529
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2432531{col 56}{space 2} .0195915{col 67}{space 1}   12.42{col 76}{space 3}0.000{col 84}{space 4}  .204854{col 97}{space 3} .2816523
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}73976{txt}) = {res}7.953{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.3840
{col 25}{txt}Prob>|t| = {res}    0.7157

95%{txt} confidence set for null hypothesis expression: {res}[−.02236, .03424]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:74,098}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2479554{col 28}{space 2} .0015864{col 39}{space 5} .2448461{col 53}{space 3} .2510647
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:30,154}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:77}, {res:30056})}{col 70} = {res}{ralign 6:4.62}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0241}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0209}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.3632}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0051923{col 56}{space 2}  .007393{col 67}{space 1}    0.70{col 76}{space 3}0.482{col 84}{space 4}-.0092982{col 97}{space 3} .0196828
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.2533575{col 56}{space 2} .1593918{col 67}{space 1}   -1.59{col 76}{space 3}0.112{col 84}{space 4}-.5657722{col 97}{space 3} .0590572
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}-.1109517{col 56}{space 2} .1879689{col 67}{space 1}   -0.59{col 76}{space 3}0.555{col 84}{space 4}-.4793788{col 97}{space 3} .2574753
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .7805255{col 56}{space 2} .1875231{col 67}{space 1}    4.16{col 76}{space 3}0.000{col 84}{space 4} .4129721{col 97}{space 3} 1.148079
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .3767152{col 56}{space 2} .1785853{col 67}{space 1}    2.11{col 76}{space 3}0.035{col 84}{space 4} .0266802{col 97}{space 3} .7267501
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .3997002{col 56}{space 2}   .19396{col 67}{space 1}    2.06{col 76}{space 3}0.039{col 84}{space 4} .0195303{col 97}{space 3}   .77987
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} .4688312{col 56}{space 2} .1984509{col 67}{space 1}    2.36{col 76}{space 3}0.018{col 84}{space 4} .0798589{col 97}{space 3} .8578035
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .1765341{col 56}{space 2} .1848657{col 67}{space 1}    0.95{col 76}{space 3}0.340{col 84}{space 4}-.1858106{col 97}{space 3} .5388787
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .4670887{col 56}{space 2} .1898275{col 67}{space 1}    2.46{col 76}{space 3}0.014{col 84}{space 4} .0950188{col 97}{space 3} .8391587
{txt}{space 37}2018  {c |}{col 44}{res}{space 2} .3816369{col 56}{space 2}  .193007{col 67}{space 1}    1.98{col 76}{space 3}0.048{col 84}{space 4} .0033348{col 97}{space 3} .7599389
{txt}{space 37}2019  {c |}{col 44}{res}{space 2} .1727234{col 56}{space 2} .1348257{col 67}{space 1}    1.28{col 76}{space 3}0.200{col 84}{space 4}-.0915407{col 97}{space 3} .4369876
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .2724203{col 56}{space 2}  .144331{col 67}{space 1}    1.89{col 76}{space 3}0.059{col 84}{space 4}-.0104746{col 97}{space 3} .5553152
{txt}{space 37}2021  {c |}{col 44}{res}{space 2}-.0325827{col 56}{space 2}  .154814{col 67}{space 1}   -0.21{col 76}{space 3}0.833{col 84}{space 4}-.3360247{col 97}{space 3} .2708593
{txt}{space 37}2022  {c |}{col 44}{res}{space 2}-.0364523{col 56}{space 2} .1412762{col 67}{space 1}   -0.26{col 76}{space 3}0.796{col 84}{space 4}-.3133597{col 97}{space 3} .2404551
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.0401115{col 56}{space 2} .0326044{col 67}{space 1}   -1.23{col 76}{space 3}0.219{col 84}{space 4}-.1040176{col 97}{space 3} .0237946
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2}-.0390099{col 56}{space 2} .0267854{col 67}{space 1}   -1.46{col 76}{space 3}0.145{col 84}{space 4}-.0915104{col 97}{space 3} .0134905
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0233426{col 56}{space 2} .0192207{col 67}{space 1}    1.21{col 76}{space 3}0.225{col 84}{space 4}-.0143308{col 97}{space 3} .0610159
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0037476{col 56}{space 2} .0213163{col 67}{space 1}    0.18{col 76}{space 3}0.860{col 84}{space 4}-.0380332{col 97}{space 3} .0455285
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0029638{col 56}{space 2} .0239042{col 67}{space 1}    0.12{col 76}{space 3}0.901{col 84}{space 4}-.0438895{col 97}{space 3} .0498172
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}-.0288102{col 56}{space 2}  .037795{col 67}{space 1}   -0.76{col 76}{space 3}0.446{col 84}{space 4}-.1028899{col 97}{space 3} .0452696
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0350727{col 56}{space 2} .0223306{col 67}{space 1}    1.57{col 76}{space 3}0.116{col 84}{space 4}-.0086962{col 97}{space 3} .0788415
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0473888{col 56}{space 2} .0209725{col 67}{space 1}   -2.26{col 76}{space 3}0.024{col 84}{space 4}-.0884957{col 97}{space 3}-.0062819
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2}  .018326{col 56}{space 2} .0328035{col 67}{space 1}    0.56{col 76}{space 3}0.576{col 84}{space 4}-.0459703{col 97}{space 3} .0826222
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0519629{col 56}{space 2} .0279762{col 67}{space 1}    1.86{col 76}{space 3}0.063{col 84}{space 4}-.0028716{col 97}{space 3} .1067973
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2}-.0117812{col 56}{space 2} .0211151{col 67}{space 1}   -0.56{col 76}{space 3}0.577{col 84}{space 4}-.0531677{col 97}{space 3} .0296052
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0012493{col 56}{space 2}  .022218{col 67}{space 1}    0.06{col 76}{space 3}0.955{col 84}{space 4}-.0422989{col 97}{space 3} .0447976
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2}-.0080124{col 56}{space 2} .0228464{col 67}{space 1}   -0.35{col 76}{space 3}0.726{col 84}{space 4}-.0527923{col 97}{space 3} .0367675
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2}  .012457{col 56}{space 2} .0263954{col 67}{space 1}    0.47{col 76}{space 3}0.637{col 84}{space 4}-.0392792{col 97}{space 3} .0641932
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0114942{col 56}{space 2} .0213202{col 67}{space 1}   -0.54{col 76}{space 3}0.590{col 84}{space 4}-.0532827{col 97}{space 3} .0302943
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0194991{col 56}{space 2} .0370578{col 67}{space 1}    0.53{col 76}{space 3}0.599{col 84}{space 4}-.0531358{col 97}{space 3} .0921339
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1091662{col 56}{space 2} .0254907{col 67}{space 1}   -4.28{col 76}{space 3}0.000{col 84}{space 4} -.159129{col 97}{space 3}-.0592033
{txt}{space 35}girona  {c |}{col 44}{res}{space 2} -.085029{col 56}{space 2} .0275281{col 67}{space 1}   -3.09{col 76}{space 3}0.002{col 84}{space 4}-.1389853{col 97}{space 3}-.0310726
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0292755{col 56}{space 2} .0211055{col 67}{space 1}    1.39{col 76}{space 3}0.165{col 84}{space 4}-.0120921{col 97}{space 3} .0706431
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0487676{col 56}{space 2} .0277221{col 67}{space 1}    1.76{col 76}{space 3}0.079{col 84}{space 4} -.005569{col 97}{space 3} .1031041
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0023455{col 56}{space 2} .0276737{col 67}{space 1}    0.08{col 76}{space 3}0.932{col 84}{space 4}-.0518962{col 97}{space 3} .0565871
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0532487{col 56}{space 2} .0344381{col 67}{space 1}   -1.55{col 76}{space 3}0.122{col 84}{space 4}-.1207489{col 97}{space 3} .0142516
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0043425{col 56}{space 2} .0263111{col 67}{space 1}    0.17{col 76}{space 3}0.869{col 84}{space 4}-.0472283{col 97}{space 3} .0559133
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0229208{col 56}{space 2} .0222543{col 67}{space 1}    1.03{col 76}{space 3}0.303{col 84}{space 4}-.0206986{col 97}{space 3} .0665402
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0457087{col 56}{space 2} .0258183{col 67}{space 1}    1.77{col 76}{space 3}0.077{col 84}{space 4}-.0048962{col 97}{space 3} .0963136
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0265919{col 56}{space 2} .0240499{col 67}{space 1}    1.11{col 76}{space 3}0.269{col 84}{space 4} -.020547{col 97}{space 3} .0737308
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0035563{col 56}{space 2} .0282954{col 67}{space 1}    0.13{col 76}{space 3}0.900{col 84}{space 4}-.0519039{col 97}{space 3} .0590164
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0683471{col 56}{space 2} .0283667{col 67}{space 1}   -2.41{col 76}{space 3}0.016{col 84}{space 4}-.1239472{col 97}{space 3}-.0127471
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}-.0249387{col 56}{space 2} .0279597{col 67}{space 1}   -0.89{col 76}{space 3}0.372{col 84}{space 4}-.0797409{col 97}{space 3} .0298636
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2}  -.01355{col 56}{space 2} .0182526{col 67}{space 1}   -0.74{col 76}{space 3}0.458{col 84}{space 4}-.0493257{col 97}{space 3} .0222258
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0213226{col 56}{space 2} .0188651{col 67}{space 1}    1.13{col 76}{space 3}0.258{col 84}{space 4}-.0156538{col 97}{space 3} .0582991
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0223566{col 56}{space 2} .0188338{col 67}{space 1}    1.19{col 76}{space 3}0.235{col 84}{space 4}-.0145585{col 97}{space 3} .0592717
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0155599{col 56}{space 2} .0255446{col 67}{space 1}   -0.61{col 76}{space 3}0.542{col 84}{space 4}-.0656284{col 97}{space 3} .0345086
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0221153{col 56}{space 2}  .029826{col 67}{space 1}    0.74{col 76}{space 3}0.458{col 84}{space 4} -.036345{col 97}{space 3} .0805755
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0365159{col 56}{space 2} .0360806{col 67}{space 1}    1.01{col 76}{space 3}0.312{col 84}{space 4}-.0342036{col 97}{space 3} .1072355
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} .0184746{col 56}{space 2} .0206381{col 67}{space 1}    0.90{col 76}{space 3}0.371{col 84}{space 4}-.0219769{col 97}{space 3} .0589262
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2}-.0002664{col 56}{space 2} .0300942{col 67}{space 1}   -0.01{col 76}{space 3}0.993{col 84}{space 4}-.0592523{col 97}{space 3} .0587195
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0476939{col 56}{space 2} .0203765{col 67}{space 1}    2.34{col 76}{space 3}0.019{col 84}{space 4} .0077551{col 97}{space 3} .0876328
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2}-.0021216{col 56}{space 2}  .049915{col 67}{space 1}   -0.04{col 76}{space 3}0.966{col 84}{space 4}-.0999571{col 97}{space 3} .0957138
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2}-.0045551{col 56}{space 2} .0205399{col 67}{space 1}   -0.22{col 76}{space 3}0.824{col 84}{space 4}-.0448142{col 97}{space 3} .0357041
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .1349307{col 56}{space 2} .0780465{col 67}{space 1}    1.73{col 76}{space 3}0.084{col 84}{space 4}-.0180437{col 97}{space 3} .2879051
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2} -.048228{col 56}{space 2} .0268546{col 67}{space 1}   -1.80{col 76}{space 3}0.073{col 84}{space 4}-.1008642{col 97}{space 3} .0044082
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0091761{col 56}{space 2} .0369422{col 67}{space 1}   -0.25{col 76}{space 3}0.804{col 84}{space 4}-.0815843{col 97}{space 3} .0632322
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2}  .034467{col 56}{space 2} .0220287{col 67}{space 1}    1.56{col 76}{space 3}0.118{col 84}{space 4}-.0087102{col 97}{space 3} .0776442
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0161434{col 56}{space 2} .0176263{col 67}{space 1}    0.92{col 76}{space 3}0.360{col 84}{space 4}-.0184049{col 97}{space 3} .0506917
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0064866{col 56}{space 2} .0232148{col 67}{space 1}    0.28{col 76}{space 3}0.780{col 84}{space 4}-.0390153{col 97}{space 3} .0519886
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.0851044{col 56}{space 2} .0202194{col 67}{space 1}   -4.21{col 76}{space 3}0.000{col 84}{space 4}-.1247353{col 97}{space 3}-.0454734
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0256039{col 56}{space 2} .0395088{col 67}{space 1}    0.65{col 76}{space 3}0.517{col 84}{space 4} -.051835{col 97}{space 3} .1030429
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0018214{col 56}{space 2} .0220034{col 67}{space 1}    0.08{col 76}{space 3}0.934{col 84}{space 4}-.0413063{col 97}{space 3}  .044949
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0108922{col 56}{space 2} .0112282{col 67}{space 1}   -0.97{col 76}{space 3}0.332{col 84}{space 4}-.0328999{col 97}{space 3} .0111155
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0014701{col 56}{space 2} .0108613{col 67}{space 1}    0.14{col 76}{space 3}0.892{col 84}{space 4}-.0198186{col 97}{space 3} .0227587
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0015711{col 56}{space 2} .0118208{col 67}{space 1}    0.13{col 76}{space 3}0.894{col 84}{space 4}-.0215981{col 97}{space 3} .0247404
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0051067{col 56}{space 2} .0111699{col 67}{space 1}    0.46{col 76}{space 3}0.648{col 84}{space 4}-.0167868{col 97}{space 3} .0270002
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0045929{col 56}{space 2} .0142069{col 67}{space 1}   -0.32{col 76}{space 3}0.746{col 84}{space 4}-.0324391{col 97}{space 3} .0232532
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0107993{col 56}{space 2}  .013934{col 67}{space 1}    0.78{col 76}{space 3}0.438{col 84}{space 4}-.0165119{col 97}{space 3} .0381105
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0154113{col 56}{space 2} .0042825{col 67}{space 1}    3.60{col 76}{space 3}0.000{col 84}{space 4} .0070174{col 97}{space 3} .0238051
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0010711{col 56}{space 2} .0003231{col 67}{space 1}    3.31{col 76}{space 3}0.001{col 84}{space 4} .0004377{col 97}{space 3} .0017044
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0171869{col 56}{space 2} .0054589{col 67}{space 1}   -3.15{col 76}{space 3}0.002{col 84}{space 4}-.0278866{col 97}{space 3}-.0064873
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0276716{col 56}{space 2} .0059119{col 67}{space 1}   -4.68{col 76}{space 3}0.000{col 84}{space 4}-.0392592{col 97}{space 3} -.016084
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0089886{col 56}{space 2} .0054799{col 67}{space 1}    1.64{col 76}{space 3}0.101{col 84}{space 4}-.0017522{col 97}{space 3} .0197295
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}  .040605{col 56}{space 2} .0209769{col 67}{space 1}    1.94{col 76}{space 3}0.053{col 84}{space 4}-.0005108{col 97}{space 3} .0817207
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0116522{col 56}{space 2} .0081805{col 67}{space 1}    1.42{col 76}{space 3}0.154{col 84}{space 4} -.004382{col 97}{space 3} .0276864
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0048125{col 56}{space 2} .0146925{col 67}{space 1}    0.33{col 76}{space 3}0.743{col 84}{space 4}-.0239854{col 97}{space 3} .0336104
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1529997{col 56}{space 2} .0400371{col 67}{space 1}    3.82{col 76}{space 3}0.000{col 84}{space 4} .0745252{col 97}{space 3} .2314742
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}30056{txt}) = {res}6.365{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.5654
{col 25}{txt}Prob>|t| = {res}    0.6116

95%{txt} confidence set for null hypothesis expression: {res}[−.0209, .02885]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:30,154}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .1605094{col 28}{space 2} .0021139{col 39}{space 5}  .156366{col 53}{space 3} .1646528
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#0b.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#1.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#3.education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:104,252}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:56}
{txt}{col 52}{lalign 17:F({res:163}, {res:104033})}{col 69} = {res}{ralign 7:15.77}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0447}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0427}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4070}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2} .0047512{col 58}{space 2} .0046366{col 69}{space 1}    1.02{col 78}{space 3}0.306{col 86}{space 4}-.0043366{col 99}{space 3} .0138389
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2}-.1366704{col 58}{space 2} .1517246{col 69}{space 1}   -0.90{col 78}{space 3}0.368{col 86}{space 4}-.4340487{col 99}{space 3} .1607079
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2} .0539623{col 58}{space 2} .2119641{col 69}{space 1}    0.25{col 78}{space 3}0.799{col 86}{space 4}-.3614846{col 99}{space 3} .4694092
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.1198388{col 58}{space 2} .1744499{col 69}{space 1}   -0.69{col 78}{space 3}0.492{col 86}{space 4}-.4617583{col 99}{space 3} .2220808
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.2403292{col 58}{space 2} .1736426{col 69}{space 1}   -1.38{col 78}{space 3}0.166{col 86}{space 4}-.5806663{col 99}{space 3}  .100008
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}-.4550005{col 58}{space 2} .2162674{col 69}{space 1}   -2.10{col 78}{space 3}0.035{col 86}{space 4}-.8788818{col 99}{space 3}-.0311193
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}-.1752299{col 58}{space 2} .1824304{col 69}{space 1}   -0.96{col 78}{space 3}0.337{col 86}{space 4}-.5327912{col 99}{space 3} .1823314
{txt}{space 39}1993  {c |}{col 46}{res}{space 2}-.0815628{col 58}{space 2} .1708265{col 69}{space 1}   -0.48{col 78}{space 3}0.633{col 86}{space 4}-.4163804{col 99}{space 3} .2532549
{txt}{space 39}1994  {c |}{col 46}{res}{space 2} .0619336{col 58}{space 2} .1726105{col 69}{space 1}    0.36{col 78}{space 3}0.720{col 86}{space 4}-.2763807{col 99}{space 3} .4002478
{txt}{space 39}1995  {c |}{col 46}{res}{space 2} .0050027{col 58}{space 2}  .169225{col 69}{space 1}    0.03{col 78}{space 3}0.976{col 86}{space 4} -.326676{col 99}{space 3} .3366815
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}-.0368029{col 58}{space 2} .1846152{col 69}{space 1}   -0.20{col 78}{space 3}0.842{col 86}{space 4}-.3986464{col 99}{space 3} .3250405
{txt}{space 39}1997  {c |}{col 46}{res}{space 2} .3455614{col 58}{space 2} .2092662{col 69}{space 1}    1.65{col 78}{space 3}0.099{col 86}{space 4}-.0645977{col 99}{space 3} .7557205
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .6163993{col 58}{space 2} .2188333{col 69}{space 1}    2.82{col 78}{space 3}0.005{col 86}{space 4}  .187489{col 99}{space 3}  1.04531
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .6591396{col 58}{space 2}  .216401{col 69}{space 1}    3.05{col 78}{space 3}0.002{col 86}{space 4} .2349965{col 99}{space 3} 1.083283
{txt}{space 39}2000  {c |}{col 46}{res}{space 2} .4214088{col 58}{space 2} .1539258{col 69}{space 1}    2.74{col 78}{space 3}0.006{col 86}{space 4} .1197163{col 99}{space 3} .7231012
{txt}{space 39}2001  {c |}{col 46}{res}{space 2} .0496245{col 58}{space 2}  .208515{col 69}{space 1}    0.24{col 78}{space 3}0.812{col 86}{space 4}-.3590622{col 99}{space 3} .4583111
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .4238269{col 58}{space 2} .2145425{col 69}{space 1}    1.98{col 78}{space 3}0.048{col 86}{space 4} .0033263{col 99}{space 3} .8443274
{txt}{space 39}2003  {c |}{col 46}{res}{space 2}-.0551337{col 58}{space 2} .2072587{col 69}{space 1}   -0.27{col 78}{space 3}0.790{col 86}{space 4}-.4613581{col 99}{space 3} .3510906
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} .3250336{col 58}{space 2} .1528704{col 69}{space 1}    2.13{col 78}{space 3}0.033{col 86}{space 4} .0254097{col 99}{space 3} .6246575
{txt}{space 39}2005  {c |}{col 46}{res}{space 2} -.277105{col 58}{space 2} .2025413{col 69}{space 1}   -1.37{col 78}{space 3}0.171{col 86}{space 4}-.6740833{col 99}{space 3} .1198734
{txt}{space 39}2006  {c |}{col 46}{res}{space 2} .0008512{col 58}{space 2} .1909664{col 69}{space 1}    0.00{col 78}{space 3}0.996{col 86}{space 4}-.3734404{col 99}{space 3} .3751429
{txt}{space 39}2007  {c |}{col 46}{res}{space 2} .2218555{col 58}{space 2} .1831971{col 69}{space 1}    1.21{col 78}{space 3}0.226{col 86}{space 4}-.1372083{col 99}{space 3} .5809193
{txt}{space 39}2008  {c |}{col 46}{res}{space 2} .1447756{col 58}{space 2} .1521309{col 69}{space 1}    0.95{col 78}{space 3}0.341{col 86}{space 4}-.1533989{col 99}{space 3}   .44295
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.2590859{col 58}{space 2} .1898917{col 69}{space 1}   -1.36{col 78}{space 3}0.172{col 86}{space 4}-.6312712{col 99}{space 3} .1130994
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}-.1170489{col 58}{space 2} .2342615{col 69}{space 1}   -0.50{col 78}{space 3}0.617{col 86}{space 4}-.5761983{col 99}{space 3} .3421005
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.2278921{col 58}{space 2} .2536688{col 69}{space 1}   -0.90{col 78}{space 3}0.369{col 86}{space 4}-.7250797{col 99}{space 3} .2692954
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .6638156{col 58}{space 2} .2520776{col 69}{space 1}    2.63{col 78}{space 3}0.008{col 86}{space 4} .1697469{col 99}{space 3} 1.157884
{txt}{space 39}2013  {c |}{col 46}{res}{space 2} .2600086{col 58}{space 2} .2453293{col 69}{space 1}    1.06{col 78}{space 3}0.289{col 86}{space 4}-.2208336{col 99}{space 3} .7408509
{txt}{space 39}2014  {c |}{col 46}{res}{space 2} .2825951{col 58}{space 2} .2642363{col 69}{space 1}    1.07{col 78}{space 3}0.285{col 86}{space 4}-.2353046{col 99}{space 3} .8004948
{txt}{space 39}2015  {c |}{col 46}{res}{space 2}  .351706{col 58}{space 2} .2672523{col 69}{space 1}    1.32{col 78}{space 3}0.188{col 86}{space 4} -.172105{col 99}{space 3}  .875517
{txt}{space 39}2016  {c |}{col 46}{res}{space 2}  .059627{col 58}{space 2} .2531192{col 69}{space 1}    0.24{col 78}{space 3}0.814{col 86}{space 4}-.4364832{col 99}{space 3} .5557372
{txt}{space 39}2017  {c |}{col 46}{res}{space 2} .3500707{col 58}{space 2} .2580207{col 69}{space 1}    1.36{col 78}{space 3}0.175{col 86}{space 4}-.1556465{col 99}{space 3}  .855788
{txt}{space 39}2018  {c |}{col 46}{res}{space 2} .2650437{col 58}{space 2} .2613259{col 69}{space 1}    1.01{col 78}{space 3}0.310{col 86}{space 4}-.2471516{col 99}{space 3}  .777239
{txt}{space 39}2019  {c |}{col 46}{res}{space 2} .0561818{col 58}{space 2} .2126621{col 69}{space 1}    0.26{col 78}{space 3}0.792{col 86}{space 4}-.3606331{col 99}{space 3} .4729967
{txt}{space 39}2020  {c |}{col 46}{res}{space 2} .1553261{col 58}{space 2} .2188939{col 69}{space 1}    0.71{col 78}{space 3}0.478{col 86}{space 4} -.273703{col 99}{space 3} .5843551
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.1496847{col 58}{space 2} .2334393{col 69}{space 1}   -0.64{col 78}{space 3}0.521{col 86}{space 4}-.6072226{col 99}{space 3} .3078532
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.1534942{col 58}{space 2} .2048267{col 69}{space 1}   -0.75{col 78}{space 3}0.454{col 86}{space 4}-.5549518{col 99}{space 3} .2479635
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.0401358{col 58}{space 2} .0365368{col 69}{space 1}   -1.10{col 78}{space 3}0.272{col 86}{space 4}-.1117474{col 99}{space 3} .0314758
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2}-.0385515{col 58}{space 2} .0291571{col 69}{space 1}   -1.32{col 78}{space 3}0.186{col 86}{space 4} -.095699{col 99}{space 3}  .018596
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0233894{col 58}{space 2} .0215277{col 69}{space 1}    1.09{col 78}{space 3}0.277{col 86}{space 4}-.0188046{col 99}{space 3} .0655833
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0038187{col 58}{space 2} .0238629{col 69}{space 1}    0.16{col 78}{space 3}0.873{col 86}{space 4}-.0429523{col 99}{space 3} .0505896
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2}  .002979{col 58}{space 2} .0267877{col 69}{space 1}    0.11{col 78}{space 3}0.911{col 86}{space 4}-.0495244{col 99}{space 3} .0554825
{txt}{space 38}avila  {c |}{col 46}{res}{space 2} -.028805{col 58}{space 2} .0423555{col 69}{space 1}   -0.68{col 78}{space 3}0.496{col 86}{space 4}-.1118213{col 99}{space 3} .0542112
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0350679{col 58}{space 2}  .025025{col 69}{space 1}    1.40{col 78}{space 3}0.161{col 86}{space 4}-.0139808{col 99}{space 3} .0841167
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.0474092{col 58}{space 2}  .023501{col 69}{space 1}   -2.02{col 78}{space 3}0.044{col 86}{space 4}-.0934709{col 99}{space 3}-.0013476
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0184127{col 58}{space 2}  .036737{col 69}{space 1}    0.50{col 78}{space 3}0.616{col 86}{space 4}-.0535913{col 99}{space 3} .0904167
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2}  .051972{col 58}{space 2} .0313516{col 69}{space 1}    1.66{col 78}{space 3}0.097{col 86}{space 4}-.0094767{col 99}{space 3} .1134208
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2}-.0117806{col 58}{space 2} .0236629{col 69}{space 1}   -0.50{col 78}{space 3}0.619{col 86}{space 4}-.0581597{col 99}{space 3} .0345984
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2} .0012597{col 58}{space 2} .0248985{col 69}{space 1}    0.05{col 78}{space 3}0.960{col 86}{space 4} -.047541{col 99}{space 3} .0500603
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2}-.0080139{col 58}{space 2} .0256032{col 69}{space 1}   -0.31{col 78}{space 3}0.754{col 86}{space 4}-.0581958{col 99}{space 3}  .042168
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0124617{col 58}{space 2} .0295804{col 69}{space 1}    0.42{col 78}{space 3}0.674{col 86}{space 4}-.0455155{col 99}{space 3} .0704389
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2}-.0114993{col 58}{space 2} .0238927{col 69}{space 1}   -0.48{col 78}{space 3}0.630{col 86}{space 4}-.0583287{col 99}{space 3} .0353302
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2} .0199651{col 58}{space 2}  .040891{col 69}{space 1}    0.49{col 78}{space 3}0.625{col 86}{space 4}-.0601806{col 99}{space 3} .1001109
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.1091844{col 58}{space 2} .0285652{col 69}{space 1}   -3.82{col 78}{space 3}0.000{col 86}{space 4}-.1651717{col 99}{space 3} -.053197
{txt}{space 37}girona  {c |}{col 46}{res}{space 2}-.0850571{col 58}{space 2} .0308468{col 69}{space 1}   -2.76{col 78}{space 3}0.006{col 86}{space 4}-.1455163{col 99}{space 3}-.0245979
{txt}{space 36}granada  {c |}{col 46}{res}{space 2}  .029334{col 58}{space 2} .0236347{col 69}{space 1}    1.24{col 78}{space 3}0.215{col 86}{space 4}-.0169896{col 99}{space 3} .0756576
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0487546{col 58}{space 2} .0310666{col 69}{space 1}    1.57{col 78}{space 3}0.117{col 86}{space 4}-.0121355{col 99}{space 3} .1096447
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2} .0024181{col 58}{space 2} .0309924{col 69}{space 1}    0.08{col 78}{space 3}0.938{col 86}{space 4}-.0583265{col 99}{space 3} .0631628
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2}-.0527462{col 58}{space 2} .0377931{col 69}{space 1}   -1.40{col 78}{space 3}0.163{col 86}{space 4}-.1268201{col 99}{space 3} .0213277
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2} .0043097{col 58}{space 2} .0294815{col 69}{space 1}    0.15{col 78}{space 3}0.884{col 86}{space 4}-.0534736{col 99}{space 3} .0620931
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0229259{col 58}{space 2} .0249395{col 69}{space 1}    0.92{col 78}{space 3}0.358{col 86}{space 4}-.0259553{col 99}{space 3} .0718071
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0457186{col 58}{space 2} .0289333{col 69}{space 1}    1.58{col 78}{space 3}0.114{col 86}{space 4}-.0109903{col 99}{space 3} .1024274
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} .0266189{col 58}{space 2} .0269487{col 69}{space 1}    0.99{col 78}{space 3}0.323{col 86}{space 4}-.0262002{col 99}{space 3}  .079438
{txt}{space 39}leon  {c |}{col 46}{res}{space 2} .0035658{col 58}{space 2} .0317094{col 69}{space 1}    0.11{col 78}{space 3}0.910{col 86}{space 4}-.0585841{col 99}{space 3} .0657157
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2}-.0683182{col 58}{space 2} .0317865{col 69}{space 1}   -2.15{col 78}{space 3}0.032{col 86}{space 4}-.1306193{col 99}{space 3}-.0060171
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2}-.0248055{col 58}{space 2} .0312649{col 69}{space 1}   -0.79{col 78}{space 3}0.428{col 86}{space 4}-.0860842{col 99}{space 3} .0364733
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2}-.0135227{col 58}{space 2} .0204506{col 69}{space 1}   -0.66{col 78}{space 3}0.508{col 86}{space 4}-.0536057{col 99}{space 3} .0265603
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0213228{col 58}{space 2} .0211415{col 69}{space 1}    1.01{col 78}{space 3}0.313{col 86}{space 4}-.0201143{col 99}{space 3} .0627599
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2} .0223539{col 58}{space 2} .0211064{col 69}{space 1}    1.06{col 78}{space 3}0.290{col 86}{space 4}-.0190143{col 99}{space 3} .0637222
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0155754{col 58}{space 2}  .028626{col 69}{space 1}   -0.54{col 78}{space 3}0.586{col 86}{space 4}-.0716819{col 99}{space 3} .0405311
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} .0220993{col 58}{space 2} .0334241{col 69}{space 1}    0.66{col 78}{space 3}0.509{col 86}{space 4}-.0434115{col 99}{space 3}   .08761
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2} .0365213{col 58}{space 2} .0404343{col 69}{space 1}    0.90{col 78}{space 3}0.366{col 86}{space 4}-.0427293{col 99}{space 3}  .115772
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2} .0184646{col 58}{space 2} .0231279{col 69}{space 1}    0.80{col 78}{space 3}0.425{col 86}{space 4}-.0268658{col 99}{space 3}  .063795
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2}-.0001932{col 58}{space 2} .0337063{col 69}{space 1}   -0.01{col 78}{space 3}0.995{col 86}{space 4}-.0662571{col 99}{space 3} .0658707
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .0477117{col 58}{space 2} .0228336{col 69}{space 1}    2.09{col 78}{space 3}0.037{col 86}{space 4} .0029582{col 99}{space 3} .0924653
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2}-.0021354{col 58}{space 2} .0559376{col 69}{space 1}   -0.04{col 78}{space 3}0.970{col 86}{space 4}-.1117724{col 99}{space 3} .1075016
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2}  -.00457{col 58}{space 2} .0230173{col 69}{space 1}   -0.20{col 78}{space 3}0.843{col 86}{space 4}-.0496835{col 99}{space 3} .0405436
{txt}{space 38}soria  {c |}{col 46}{res}{space 2}  .134949{col 58}{space 2} .0874636{col 69}{space 1}    1.54{col 78}{space 3}0.123{col 86}{space 4}-.0364785{col 99}{space 3} .3063765
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2}-.0480955{col 58}{space 2} .0300243{col 69}{space 1}   -1.60{col 78}{space 3}0.109{col 86}{space 4}-.1069427{col 99}{space 3} .0107517
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2}-.0091851{col 58}{space 2} .0413996{col 69}{space 1}   -0.22{col 78}{space 3}0.824{col 86}{space 4}-.0903278{col 99}{space 3} .0719577
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2} .0344718{col 58}{space 2} .0246867{col 69}{space 1}    1.40{col 78}{space 3}0.163{col 86}{space 4}-.0139139{col 99}{space 3} .0828574
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2} .0161648{col 58}{space 2} .0197504{col 69}{space 1}    0.82{col 78}{space 3}0.413{col 86}{space 4}-.0225456{col 99}{space 3} .0548753
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2} .0064766{col 58}{space 2} .0260156{col 69}{space 1}    0.25{col 78}{space 3}0.803{col 86}{space 4}-.0445135{col 99}{space 3} .0574668
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.0849963{col 58}{space 2} .0225968{col 69}{space 1}   -3.76{col 78}{space 3}0.000{col 86}{space 4}-.1292857{col 99}{space 3}-.0407069
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2} .0256858{col 58}{space 2} .0442579{col 69}{space 1}    0.58{col 78}{space 3}0.562{col 86}{space 4}-.0610591{col 99}{space 3} .1124306
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2} .0018194{col 58}{space 2} .0246585{col 69}{space 1}    0.07{col 78}{space 3}0.941{col 86}{space 4}-.0465109{col 99}{space 3} .0501497
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2}-.0108858{col 58}{space 2} .0125826{col 69}{space 1}   -0.87{col 78}{space 3}0.387{col 86}{space 4}-.0355476{col 99}{space 3}  .013776
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0014767{col 58}{space 2} .0121715{col 69}{space 1}    0.12{col 78}{space 3}0.903{col 86}{space 4}-.0223792{col 99}{space 3} .0253326
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0015762{col 58}{space 2} .0132469{col 69}{space 1}    0.12{col 78}{space 3}0.905{col 86}{space 4}-.0243876{col 99}{space 3}   .02754
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2}  .005112{col 58}{space 2} .0125175{col 69}{space 1}    0.41{col 78}{space 3}0.683{col 86}{space 4}-.0194221{col 99}{space 3} .0296461
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0045865{col 58}{space 2} .0159209{col 69}{space 1}   -0.29{col 78}{space 3}0.773{col 86}{space 4}-.0357913{col 99}{space 3} .0266182
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2} .0108071{col 58}{space 2} .0156149{col 69}{space 1}    0.69{col 78}{space 3}0.489{col 86}{space 4}-.0197978{col 99}{space 3} .0414121
{txt}{space 44} {c |}
{space 38}female {c |}{col 46}{res}{space 2}-.0075386{col 58}{space 2} .0032872{col 69}{space 1}   -2.29{col 78}{space 3}0.022{col 86}{space 4}-.0139814{col 99}{space 3}-.0010957
{txt}{space 41}age {c |}{col 46}{res}{space 2} .0010709{col 58}{space 2} .0003621{col 69}{space 1}    2.96{col 78}{space 3}0.003{col 86}{space 4} .0003611{col 99}{space 3} .0017807
{txt}{space 44} {c |}
{space 35}education {c |}
{space 34}Secondary  {c |}{col 46}{res}{space 2}-.0171854{col 58}{space 2} .0061176{col 69}{space 1}   -2.81{col 78}{space 3}0.005{col 86}{space 4}-.0291757{col 99}{space 3}-.0051951
{txt}{space 27}Higher Education  {c |}{col 46}{res}{space 2}-.0276683{col 58}{space 2} .0066251{col 69}{space 1}   -4.18{col 78}{space 3}0.000{col 86}{space 4}-.0406534{col 99}{space 3}-.0146832
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0089857{col 58}{space 2}  .006141{col 69}{space 1}    1.46{col 78}{space 3}0.143{col 86}{space 4}-.0030505{col 99}{space 3}  .021022
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0406222{col 58}{space 2} .0235066{col 69}{space 1}    1.73{col 78}{space 3}0.084{col 86}{space 4}-.0054505{col 99}{space 3} .0866949
{txt}{space 36}Student  {c |}{col 46}{res}{space 2} .0116487{col 58}{space 2} .0091675{col 69}{space 1}    1.27{col 78}{space 3}0.204{col 86}{space 4}-.0063194{col 99}{space 3} .0296169
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2} .0048058{col 58}{space 2} .0164651{col 69}{space 1}    0.29{col 78}{space 3}0.770{col 86}{space 4}-.0274655{col 99}{space 3} .0370771
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2} .0215741{col 58}{space 2} .0181725{col 69}{space 1}    1.19{col 78}{space 3}0.235{col 86}{space 4}-.0140438{col 99}{space 3}  .057192
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0296532{col 58}{space 2} .0147464{col 69}{space 1}    2.01{col 78}{space 3}0.044{col 86}{space 4} .0007504{col 99}{space 3}  .058556
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0139108{col 58}{space 2} .0138186{col 69}{space 1}    1.01{col 78}{space 3}0.314{col 86}{space 4}-.0131736{col 99}{space 3} .0409951
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0020852{col 58}{space 2}  .014833{col 69}{space 1}    0.14{col 78}{space 3}0.888{col 86}{space 4}-.0269872{col 99}{space 3} .0311577
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0171907{col 58}{space 2} .0136254{col 69}{space 1}    1.26{col 78}{space 3}0.207{col 86}{space 4}-.0095149{col 99}{space 3} .0438962
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0315897{col 58}{space 2} .0190753{col 69}{space 1}   -1.66{col 78}{space 3}0.098{col 86}{space 4}-.0689771{col 99}{space 3} .0057977
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#female {c |}
{space 40}0 1  {c |}{col 46}{res}{space 2} .0229502{col 58}{space 2} .0058171{col 69}{space 1}    3.95{col 78}{space 3}0.000{col 86}{space 4} .0115488{col 99}{space 3} .0343515
{txt}{space 40}1 0  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 40}1 1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2}-.0002847{col 58}{space 2} .0004279{col 69}{space 1}   -0.67{col 78}{space 3}0.506{col 86}{space 4}-.0011235{col 99}{space 3} .0005541
{txt}{space 44} {c |}
{space 25}preperiod#education {c |}
{space 19}1#Primary School or less  {c |}{col 46}{res}{space 2}-.0059513{col 58}{space 2} .0079752{col 69}{space 1}   -0.75{col 78}{space 3}0.456{col 86}{space 4}-.0215826{col 99}{space 3} .0096801
{txt}{space 32}1#Secondary  {c |}{col 46}{res}{space 2}-.0112085{col 58}{space 2} .0071161{col 69}{space 1}   -1.58{col 78}{space 3}0.115{col 86}{space 4} -.025156{col 99}{space 3}  .002739
{txt}{space 25}1#Higher Education  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2}-.0149698{col 58}{space 2} .0172923{col 69}{space 1}   -0.87{col 78}{space 3}0.387{col 86}{space 4}-.0488624{col 99}{space 3} .0189227
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2}-.0360545{col 58}{space 2} .0179294{col 69}{space 1}   -2.01{col 78}{space 3}0.044{col 86}{space 4}-.0711959{col 99}{space 3} -.000913
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2} -.006483{col 58}{space 2} .0327363{col 69}{space 1}   -0.20{col 78}{space 3}0.843{col 86}{space 4}-.0706457{col 99}{space 3} .0576797
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2}-.0500077{col 58}{space 2} .0198765{col 69}{space 1}   -2.52{col 78}{space 3}0.012{col 86}{space 4}-.0889653{col 99}{space 3}  -.01105
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2}-.0005926{col 58}{space 2} .0293379{col 69}{space 1}   -0.02{col 78}{space 3}0.984{col 86}{space 4}-.0580946{col 99}{space 3} .0569094
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2}-.0964488{col 58}{space 2} .0417943{col 69}{space 1}   -2.31{col 78}{space 3}0.021{col 86}{space 4}-.1783651{col 99}{space 3}-.0145325
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .0916584{col 58}{space 2} .0363493{col 69}{space 1}    2.52{col 78}{space 3}0.012{col 86}{space 4} .0204143{col 99}{space 3} .1629025
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0500435{col 58}{space 2} .0289396{col 69}{space 1}    1.73{col 78}{space 3}0.084{col 86}{space 4}-.0066777{col 99}{space 3} .1067646
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0472051{col 58}{space 2} .0315326{col 69}{space 1}    1.50{col 78}{space 3}0.134{col 86}{space 4}-.0145984{col 99}{space 3} .1090086
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2} .0096602{col 58}{space 2} .0337173{col 69}{space 1}    0.29{col 78}{space 3}0.774{col 86}{space 4}-.0564254{col 99}{space 3} .0757457
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0544809{col 58}{space 2} .0496847{col 69}{space 1}    1.10{col 78}{space 3}0.273{col 86}{space 4}-.0429004{col 99}{space 3} .1518623
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0411012{col 58}{space 2} .0321062{col 69}{space 1}    1.28{col 78}{space 3}0.200{col 86}{space 4}-.0218265{col 99}{space 3}  .104029
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} .0238551{col 58}{space 2} .0283296{col 69}{space 1}    0.84{col 78}{space 3}0.400{col 86}{space 4}-.0316707{col 99}{space 3} .0793808
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0178354{col 58}{space 2} .0434871{col 69}{space 1}   -0.41{col 78}{space 3}0.682{col 86}{space 4}-.1030695{col 99}{space 3} .0673987
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2} .0123209{col 58}{space 2} .0381757{col 69}{space 1}    0.32{col 78}{space 3}0.747{col 86}{space 4} -.062503{col 99}{space 3} .0871448
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .0741042{col 58}{space 2}  .030876{col 69}{space 1}    2.40{col 78}{space 3}0.016{col 86}{space 4} .0135877{col 99}{space 3} .1346208
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .0410455{col 58}{space 2} .0324936{col 69}{space 1}    1.26{col 78}{space 3}0.207{col 86}{space 4}-.0226416{col 99}{space 3} .1047326
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .0670931{col 58}{space 2}  .032646{col 69}{space 1}    2.06{col 78}{space 3}0.040{col 86}{space 4} .0031074{col 99}{space 3} .1310788
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2} .0606637{col 58}{space 2} .0367371{col 69}{space 1}    1.65{col 78}{space 3}0.099{col 86}{space 4}-.0113405{col 99}{space 3}  .132668
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2}-.0276569{col 58}{space 2} .0308757{col 69}{space 1}   -0.90{col 78}{space 3}0.370{col 86}{space 4}-.0881729{col 99}{space 3} .0328591
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2}   .01622{col 58}{space 2} .0478809{col 69}{space 1}    0.34{col 78}{space 3}0.735{col 86}{space 4}-.0776258{col 99}{space 3} .1100659
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0761966{col 58}{space 2} .0339299{col 69}{space 1}   -2.25{col 78}{space 3}0.025{col 86}{space 4}-.1426988{col 99}{space 3}-.0096945
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2} -.003526{col 58}{space 2} .0363792{col 69}{space 1}   -0.10{col 78}{space 3}0.923{col 86}{space 4}-.0748287{col 99}{space 3} .0677767
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2}-.0146923{col 58}{space 2} .0311368{col 69}{space 1}   -0.47{col 78}{space 3}0.637{col 86}{space 4}  -.07572{col 99}{space 3} .0463354
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2}-.0207088{col 58}{space 2} .0387916{col 69}{space 1}   -0.53{col 78}{space 3}0.593{col 86}{space 4}-.0967398{col 99}{space 3} .0553222
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} .0221929{col 58}{space 2} .0375934{col 69}{space 1}    0.59{col 78}{space 3}0.555{col 86}{space 4}-.0514898{col 99}{space 3} .0958755
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2} .0414945{col 58}{space 2} .0448905{col 69}{space 1}    0.92{col 78}{space 3}0.355{col 86}{space 4}-.0464903{col 99}{space 3} .1294792
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .0415827{col 58}{space 2} .0341753{col 69}{space 1}    1.22{col 78}{space 3}0.224{col 86}{space 4}-.0254005{col 99}{space 3} .1085659
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2}-.0000552{col 58}{space 2} .0322053{col 69}{space 1}   -0.00{col 78}{space 3}0.999{col 86}{space 4}-.0631771{col 99}{space 3} .0630668
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0795605{col 58}{space 2} .0369574{col 69}{space 1}   -2.15{col 78}{space 3}0.031{col 86}{space 4}-.1519966{col 99}{space 3}-.0071245
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .0252188{col 58}{space 2}  .032985{col 69}{space 1}    0.76{col 78}{space 3}0.445{col 86}{space 4}-.0394313{col 99}{space 3}  .089869
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2} .0244258{col 58}{space 2} .0386067{col 69}{space 1}    0.63{col 78}{space 3}0.527{col 86}{space 4}-.0512428{col 99}{space 3} .1000943
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2}-.0196259{col 58}{space 2} .0397523{col 69}{space 1}   -0.49{col 78}{space 3}0.622{col 86}{space 4}-.0975398{col 99}{space 3}  .058288
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0297544{col 58}{space 2} .0384547{col 69}{space 1}    0.77{col 78}{space 3}0.439{col 86}{space 4}-.0456163{col 99}{space 3}  .105125
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0089148{col 58}{space 2} .0263376{col 69}{space 1}    0.34{col 78}{space 3}0.735{col 86}{space 4}-.0427066{col 99}{space 3} .0605363
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .0699745{col 58}{space 2} .0262261{col 69}{space 1}    2.67{col 78}{space 3}0.008{col 86}{space 4} .0185716{col 99}{space 3} .1213774
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2}-.0011945{col 58}{space 2} .0271141{col 69}{space 1}   -0.04{col 78}{space 3}0.965{col 86}{space 4}-.0543378{col 99}{space 3} .0519488
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2}-.0768904{col 58}{space 2} .0347635{col 69}{space 1}   -2.21{col 78}{space 3}0.027{col 86}{space 4}-.1450263{col 99}{space 3}-.0087544
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2}-.0363059{col 58}{space 2} .0395991{col 69}{space 1}   -0.92{col 78}{space 3}0.359{col 86}{space 4}-.1139197{col 99}{space 3} .0413079
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2}  -.01158{col 58}{space 2} .0480647{col 69}{space 1}   -0.24{col 78}{space 3}0.810{col 86}{space 4}-.1057862{col 99}{space 3} .0826262
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}-.0434598{col 58}{space 2} .0298593{col 69}{space 1}   -1.46{col 78}{space 3}0.146{col 86}{space 4}-.1019836{col 99}{space 3} .0150641
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .0778384{col 58}{space 2} .0404968{col 69}{space 1}    1.92{col 78}{space 3}0.055{col 86}{space 4}-.0015347{col 99}{space 3} .1572115
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0231108{col 58}{space 2} .0298088{col 69}{space 1}   -0.78{col 78}{space 3}0.438{col 86}{space 4}-.0815357{col 99}{space 3} .0353142
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0139282{col 58}{space 2} .0621791{col 69}{space 1}   -0.22{col 78}{space 3}0.823{col 86}{space 4}-.1357985{col 99}{space 3} .1079421
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2}  .073025{col 58}{space 2} .0275098{col 69}{space 1}    2.65{col 78}{space 3}0.008{col 86}{space 4} .0191061{col 99}{space 3} .1269438
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2}-.0797014{col 58}{space 2} .0934229{col 69}{space 1}   -0.85{col 78}{space 3}0.394{col 86}{space 4}-.2628091{col 99}{space 3} .1034062
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2} .0118693{col 58}{space 2} .0357308{col 69}{space 1}    0.33{col 78}{space 3}0.740{col 86}{space 4}-.0581625{col 99}{space 3} .0819011
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0028444{col 58}{space 2} .0478974{col 69}{space 1}    0.06{col 78}{space 3}0.953{col 86}{space 4}-.0910338{col 99}{space 3} .0967225
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2} .0125148{col 58}{space 2} .0325856{col 69}{space 1}    0.38{col 78}{space 3}0.701{col 86}{space 4}-.0513525{col 99}{space 3} .0763821
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2} .0240066{col 58}{space 2} .0251585{col 69}{space 1}    0.95{col 78}{space 3}0.340{col 86}{space 4}-.0253037{col 99}{space 3}  .073317
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2} .0192984{col 58}{space 2} .0334409{col 69}{space 1}    0.58{col 78}{space 3}0.564{col 86}{space 4}-.0462452{col 99}{space 3} .0848421
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0858631{col 58}{space 2} .0290482{col 69}{space 1}   -2.96{col 78}{space 3}0.003{col 86}{space 4}-.1427971{col 99}{space 3} -.028929
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2} .0667429{col 58}{space 2} .0512009{col 69}{space 1}    1.30{col 78}{space 3}0.192{col 86}{space 4}-.0336101{col 99}{space 3}  .167096
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .2170866{col 58}{space 2}  .035328{col 69}{space 1}    6.14{col 78}{space 3}0.000{col 86}{space 4} .1478442{col 99}{space 3} .2863289
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}104033{txt}) = {res}7.258{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.5320
{col 25}{txt}Prob>|t| = {res}    0.6126

95%{txt} confidence set for null hypothesis expression: {res}[−.0159, .02524]

{txt}{col 1}Mean estimation{col 42}{lalign 13:Number of obs}{col 55} = {res}{ralign 7:104,252}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2226624{col 28}{space 2} .0012885{col 39}{space 5} .2201369{col 53}{space 3} .2251879
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_22_23_young.tex"'})
(951,288 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:68,954}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:87}, {res:68832})}{col 70} = {res}{ralign 6:24.59}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0350}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0333}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4515}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0045741{col 56}{space 2} .0060577{col 67}{space 1}    0.76{col 76}{space 3}0.450{col 84}{space 4} -.007299{col 97}{space 3} .0164473
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0359395{col 56}{space 2} .1875269{col 67}{space 1}    0.19{col 76}{space 3}0.848{col 84}{space 4}-.3316129{col 97}{space 3} .4034918
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}-.1664837{col 56}{space 2} .2612699{col 67}{space 1}   -0.64{col 76}{space 3}0.524{col 84}{space 4}-.6785724{col 97}{space 3}  .345605
{txt}{space 37}1989  {c |}{col 44}{res}{space 2}-.1153085{col 56}{space 2} .2151474{col 67}{space 1}   -0.54{col 76}{space 3}0.592{col 84}{space 4}-.5369971{col 97}{space 3} .3063802
{txt}{space 37}1990  {c |}{col 44}{res}{space 2}-.2812634{col 56}{space 2} .2154137{col 67}{space 1}   -1.31{col 76}{space 3}0.192{col 84}{space 4}-.7034739{col 97}{space 3} .1409471
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.1662948{col 56}{space 2}  .265945{col 67}{space 1}   -0.63{col 76}{space 3}0.532{col 84}{space 4}-.6875466{col 97}{space 3} .3549569
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.3339053{col 56}{space 2} .2240203{col 67}{space 1}   -1.49{col 76}{space 3}0.136{col 84}{space 4}-.7729847{col 97}{space 3}  .105174
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}-.0242326{col 56}{space 2} .2100705{col 67}{space 1}   -0.12{col 76}{space 3}0.908{col 84}{space 4}-.4359705{col 97}{space 3} .3875052
{txt}{space 37}1994  {c |}{col 44}{res}{space 2}-.0923141{col 56}{space 2} .2137272{col 67}{space 1}   -0.43{col 76}{space 3}0.666{col 84}{space 4} -.511219{col 97}{space 3} .3265908
{txt}{space 37}1995  {c |}{col 44}{res}{space 2}-.1788521{col 56}{space 2} .2085648{col 67}{space 1}   -0.86{col 76}{space 3}0.391{col 84}{space 4}-.5876388{col 97}{space 3} .2299346
{txt}{space 37}1996  {c |}{col 44}{res}{space 2}-.0277473{col 56}{space 2} .2262061{col 67}{space 1}   -0.12{col 76}{space 3}0.902{col 84}{space 4} -.471111{col 97}{space 3} .4156163
{txt}{space 37}1997  {c |}{col 44}{res}{space 2} .5863076{col 56}{space 2} .2540986{col 67}{space 1}    2.31{col 76}{space 3}0.021{col 84}{space 4} .0882748{col 97}{space 3}  1.08434
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .0886618{col 56}{space 2} .2713784{col 67}{space 1}    0.33{col 76}{space 3}0.744{col 84}{space 4}-.4432394{col 97}{space 3}  .620563
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .1208478{col 56}{space 2} .2651105{col 67}{space 1}    0.46{col 76}{space 3}0.649{col 84}{space 4}-.3987683{col 97}{space 3} .6404639
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .4870218{col 56}{space 2} .1897146{col 67}{space 1}    2.57{col 76}{space 3}0.010{col 84}{space 4} .1151814{col 97}{space 3} .8588622
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} .4721347{col 56}{space 2} .2496398{col 67}{space 1}    1.89{col 76}{space 3}0.059{col 84}{space 4}-.0171588{col 97}{space 3} .9614283
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} .2275259{col 56}{space 2} .2533978{col 67}{space 1}    0.90{col 76}{space 3}0.369{col 84}{space 4}-.2691333{col 97}{space 3} .7241851
{txt}{space 37}2003  {c |}{col 44}{res}{space 2}  .232271{col 56}{space 2} .2402534{col 67}{space 1}    0.97{col 76}{space 3}0.334{col 84}{space 4}-.2386253{col 97}{space 3} .7031673
{txt}{space 37}2004  {c |}{col 44}{res}{space 2} .4211054{col 56}{space 2} .1885896{col 67}{space 1}    2.23{col 76}{space 3}0.026{col 84}{space 4} .0514701{col 97}{space 3} .7907406
{txt}{space 37}2005  {c |}{col 44}{res}{space 2}-.0670922{col 56}{space 2} .2458348{col 67}{space 1}   -0.27{col 76}{space 3}0.785{col 84}{space 4} -.548928{col 97}{space 3} .4147437
{txt}{space 37}2006  {c |}{col 44}{res}{space 2}-.3301009{col 56}{space 2} .2297806{col 67}{space 1}   -1.44{col 76}{space 3}0.151{col 84}{space 4}-.7804705{col 97}{space 3} .1202686
{txt}{space 37}2007  {c |}{col 44}{res}{space 2}-.1686581{col 56}{space 2} .2214608{col 67}{space 1}   -0.76{col 76}{space 3}0.446{col 84}{space 4} -.602721{col 97}{space 3} .2654047
{txt}{space 37}2008  {c |}{col 44}{res}{space 2}-.0420114{col 56}{space 2} .1876482{col 67}{space 1}   -0.22{col 76}{space 3}0.823{col 84}{space 4}-.4098016{col 97}{space 3} .3257788
{txt}{space 37}2009  {c |}{col 44}{res}{space 2} -.348339{col 56}{space 2}  .229084{col 67}{space 1}   -1.52{col 76}{space 3}0.128{col 84}{space 4}-.7973432{col 97}{space 3} .1006653
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.0812429{col 56}{space 2} .0192925{col 67}{space 1}   -4.21{col 76}{space 3}0.000{col 84}{space 4}-.1190562{col 97}{space 3}-.0434297
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0712903{col 56}{space 2} .0186613{col 67}{space 1}    3.82{col 76}{space 3}0.000{col 84}{space 4} .0347143{col 97}{space 3} .1078664
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .1178262{col 56}{space 2} .0149464{col 67}{space 1}    7.88{col 76}{space 3}0.000{col 84}{space 4} .0885313{col 97}{space 3} .1471211
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0795813{col 56}{space 2} .0185018{col 67}{space 1}    4.30{col 76}{space 3}0.000{col 84}{space 4} .0433178{col 97}{space 3} .1158449
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2}  .011489{col 56}{space 2} .0145629{col 67}{space 1}    0.79{col 76}{space 3}0.430{col 84}{space 4}-.0170542{col 97}{space 3} .0400322
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0829292{col 56}{space 2} .0213195{col 67}{space 1}    3.89{col 76}{space 3}0.000{col 84}{space 4} .0411429{col 97}{space 3} .1247154
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .1313948{col 56}{space 2} .0173729{col 67}{space 1}    7.56{col 76}{space 3}0.000{col 84}{space 4}  .097344{col 97}{space 3} .1654456
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2} -.009956{col 56}{space 2} .0124784{col 67}{space 1}   -0.80{col 76}{space 3}0.425{col 84}{space 4}-.0344136{col 97}{space 3} .0145016
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0173191{col 56}{space 2} .0183735{col 67}{space 1}    0.94{col 76}{space 3}0.346{col 84}{space 4}-.0186929{col 97}{space 3} .0533312
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .1057912{col 56}{space 2} .0176717{col 67}{space 1}    5.99{col 76}{space 3}0.000{col 84}{space 4} .0711546{col 97}{space 3} .1404277
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0663643{col 56}{space 2} .0177098{col 67}{space 1}    3.75{col 76}{space 3}0.000{col 84}{space 4}  .031653{col 97}{space 3} .1010755
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .1140129{col 56}{space 2} .0172174{col 67}{space 1}    6.62{col 76}{space 3}0.000{col 84}{space 4} .0802668{col 97}{space 3}  .147759
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .1074288{col 56}{space 2} .0174599{col 67}{space 1}    6.15{col 76}{space 3}0.000{col 84}{space 4} .0732074{col 97}{space 3} .1416501
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2}  .072417{col 56}{space 2} .0181376{col 67}{space 1}    3.99{col 76}{space 3}0.000{col 84}{space 4} .0368673{col 97}{space 3} .1079667
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0221704{col 56}{space 2} .0162446{col 67}{space 1}   -1.36{col 76}{space 3}0.172{col 84}{space 4}-.0540098{col 97}{space 3}  .009669
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0749041{col 56}{space 2} .0206517{col 67}{space 1}    3.63{col 76}{space 3}0.000{col 84}{space 4} .0344268{col 97}{space 3} .1153814
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1663409{col 56}{space 2}  .016022{col 67}{space 1}  -10.38{col 76}{space 3}0.000{col 84}{space 4}-.1977441{col 97}{space 3}-.1349377
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0665395{col 56}{space 2} .0182142{col 67}{space 1}   -3.65{col 76}{space 3}0.000{col 84}{space 4}-.1022393{col 97}{space 3}-.0308397
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0493555{col 56}{space 2} .0164597{col 67}{space 1}    3.00{col 76}{space 3}0.003{col 84}{space 4} .0170946{col 97}{space 3} .0816164
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0722685{col 56}{space 2} .0213736{col 67}{space 1}    3.38{col 76}{space 3}0.001{col 84}{space 4} .0303762{col 97}{space 3} .1141608
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0834666{col 56}{space 2} .0206541{col 67}{space 1}    4.04{col 76}{space 3}0.000{col 84}{space 4} .0429847{col 97}{space 3} .1239486
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0027474{col 56}{space 2} .0197663{col 67}{space 1}   -0.14{col 76}{space 3}0.889{col 84}{space 4}-.0414893{col 97}{space 3} .0359944
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0609806{col 56}{space 2} .0177277{col 67}{space 1}    3.44{col 76}{space 3}0.001{col 84}{space 4} .0262344{col 97}{space 3} .0957267
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0481432{col 56}{space 2} .0167369{col 67}{space 1}    2.88{col 76}{space 3}0.004{col 84}{space 4} .0153389{col 97}{space 3} .0809475
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0045112{col 56}{space 2} .0195622{col 67}{space 1}    0.23{col 76}{space 3}0.818{col 84}{space 4}-.0338308{col 97}{space 3} .0428532
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .1030992{col 56}{space 2} .0183122{col 67}{space 1}    5.63{col 76}{space 3}0.000{col 84}{space 4} .0672074{col 97}{space 3} .1389911
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0166499{col 56}{space 2}  .017112{col 67}{space 1}    0.97{col 76}{space 3}0.331{col 84}{space 4}-.0168896{col 97}{space 3} .0501894
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.1046479{col 56}{space 2}  .020206{col 67}{space 1}   -5.18{col 76}{space 3}0.000{col 84}{space 4}-.1442517{col 97}{space 3}-.0650442
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0747036{col 56}{space 2} .0173757{col 67}{space 1}    4.30{col 76}{space 3}0.000{col 84}{space 4} .0406472{col 97}{space 3}   .10876
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0481352{col 56}{space 2} .0132783{col 67}{space 1}    3.63{col 76}{space 3}0.000{col 84}{space 4} .0221097{col 97}{space 3} .0741607
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .1023377{col 56}{space 2} .0159039{col 67}{space 1}    6.43{col 76}{space 3}0.000{col 84}{space 4} .0711661{col 97}{space 3} .1335092
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0621364{col 56}{space 2} .0153279{col 67}{space 1}    4.05{col 76}{space 3}0.000{col 84}{space 4} .0320938{col 97}{space 3}  .092179
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0106488{col 56}{space 2} .0176615{col 67}{space 1}   -0.60{col 76}{space 3}0.547{col 84}{space 4}-.0452653{col 97}{space 3} .0239677
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0832989{col 56}{space 2}   .01747{col 67}{space 1}    4.77{col 76}{space 3}0.000{col 84}{space 4} .0490577{col 97}{space 3}   .11754
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0412326{col 56}{space 2} .0219586{col 67}{space 1}    1.88{col 76}{space 3}0.060{col 84}{space 4}-.0018062{col 97}{space 3} .0842713
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} -.010231{col 56}{space 2}  .015325{col 67}{space 1}   -0.67{col 76}{space 3}0.504{col 84}{space 4} -.040268{col 97}{space 3} .0198061
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .1263392{col 56}{space 2} .0180835{col 67}{space 1}    6.99{col 76}{space 3}0.000{col 84}{space 4} .0908955{col 97}{space 3} .1617828
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0312736{col 56}{space 2} .0174068{col 67}{space 1}    1.80{col 76}{space 3}0.072{col 84}{space 4}-.0028436{col 97}{space 3} .0653908
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0166983{col 56}{space 2} .0223726{col 67}{space 1}    0.75{col 76}{space 3}0.455{col 84}{space 4} -.027152{col 97}{space 3} .0605487
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0840044{col 56}{space 2} .0156495{col 67}{space 1}    5.37{col 76}{space 3}0.000{col 84}{space 4} .0533313{col 97}{space 3} .1146774
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0530849{col 56}{space 2} .0268693{col 67}{space 1}    1.98{col 76}{space 3}0.048{col 84}{space 4}  .000421{col 97}{space 3} .1057488
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2} .0064488{col 56}{space 2} .0176915{col 67}{space 1}    0.36{col 76}{space 3}0.715{col 84}{space 4}-.0282265{col 97}{space 3} .0411241
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0436492{col 56}{space 2} .0192217{col 67}{space 1}    2.27{col 76}{space 3}0.023{col 84}{space 4} .0059747{col 97}{space 3} .0813238
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0606659{col 56}{space 2} .0179076{col 67}{space 1}    3.39{col 76}{space 3}0.001{col 84}{space 4} .0255672{col 97}{space 3} .0957647
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0737923{col 56}{space 2} .0142253{col 67}{space 1}    5.19{col 76}{space 3}0.000{col 84}{space 4} .0459107{col 97}{space 3}  .101674
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}  .053759{col 56}{space 2} .0182582{col 67}{space 1}    2.94{col 76}{space 3}0.003{col 84}{space 4} .0179731{col 97}{space 3}  .089545
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1793833{col 56}{space 2} .0144854{col 67}{space 1}  -12.38{col 76}{space 3}0.000{col 84}{space 4}-.2077748{col 97}{space 3}-.1509919
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0966574{col 56}{space 2} .0203543{col 67}{space 1}    4.75{col 76}{space 3}0.000{col 84}{space 4} .0567631{col 97}{space 3} .1365518
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0142887{col 56}{space 2} .0170327{col 67}{space 1}    0.84{col 76}{space 3}0.402{col 84}{space 4}-.0190953{col 97}{space 3} .0476727
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0044365{col 56}{space 2}  .006655{col 67}{space 1}   -0.67{col 76}{space 3}0.505{col 84}{space 4}-.0174804{col 97}{space 3} .0086074
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0074801{col 56}{space 2} .0066991{col 67}{space 1}   -1.12{col 76}{space 3}0.264{col 84}{space 4}-.0206103{col 97}{space 3}   .00565
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} -.029388{col 56}{space 2} .0080847{col 67}{space 1}   -3.63{col 76}{space 3}0.000{col 84}{space 4} -.045234{col 97}{space 3}-.0135419
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0036568{col 56}{space 2} .0069021{col 67}{space 1}   -0.53{col 76}{space 3}0.596{col 84}{space 4}-.0171848{col 97}{space 3} .0098713
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0282434{col 56}{space 2} .0111214{col 67}{space 1}   -2.54{col 76}{space 3}0.011{col 84}{space 4}-.0500414{col 97}{space 3}-.0064454
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0352495{col 56}{space 2} .0103557{col 67}{space 1}   -3.40{col 76}{space 3}0.001{col 84}{space 4}-.0555466{col 97}{space 3}-.0149523
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0276639{col 56}{space 2} .0041884{col 67}{space 1}   -6.60{col 76}{space 3}0.000{col 84}{space 4}-.0358732{col 97}{space 3}-.0194545
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0006541{col 56}{space 2} .0002141{col 67}{space 1}    3.06{col 76}{space 3}0.002{col 84}{space 4} .0002345{col 97}{space 3} .0010737
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0016558{col 56}{space 2} .0058631{col 67}{space 1}   -0.28{col 76}{space 3}0.778{col 84}{space 4}-.0131476{col 97}{space 3} .0098359
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0162851{col 56}{space 2} .0067202{col 67}{space 1}   -2.42{col 76}{space 3}0.015{col 84}{space 4}-.0294566{col 97}{space 3}-.0031136
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0050415{col 56}{space 2} .0088801{col 67}{space 1}   -0.57{col 76}{space 3}0.570{col 84}{space 4}-.0224465{col 97}{space 3} .0123636
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0211843{col 56}{space 2} .0056376{col 67}{space 1}    3.76{col 76}{space 3}0.000{col 84}{space 4} .0101346{col 97}{space 3} .0322339
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0243146{col 56}{space 2} .0218319{col 67}{space 1}    1.11{col 76}{space 3}0.265{col 84}{space 4}-.0184758{col 97}{space 3} .0671051
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0074929{col 56}{space 2} .0058742{col 67}{space 1}    1.28{col 76}{space 3}0.202{col 84}{space 4}-.0040205{col 97}{space 3} .0190063
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  .202172{col 56}{space 2} .0223182{col 67}{space 1}    9.06{col 76}{space 3}0.000{col 84}{space 4} .1584283{col 97}{space 3} .2459156
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}68832{txt}) = {res}6.709{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.4832
{col 25}{txt}Prob>|t| = {res}    0.6346

95%{txt} confidence set for null hypothesis expression: {res}[−.01788, .03127]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:68,954}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .3021145{col 28}{space 2} .0017486{col 39}{space 5} .2986871{col 53}{space 3} .3055418
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:44,418}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:77}, {res:44320})}{col 70} = {res}{ralign 6:13.14}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0297}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0275}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4186}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0032634{col 56}{space 2} .0067382{col 67}{space 1}   -0.48{col 76}{space 3}0.628{col 84}{space 4}-.0164703{col 97}{space 3} .0099436
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.4809535{col 56}{space 2} .1694547{col 67}{space 1}   -2.84{col 76}{space 3}0.005{col 84}{space 4}-.8130877{col 97}{space 3}-.1488192
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2} .1837605{col 56}{space 2} .2090888{col 67}{space 1}    0.88{col 76}{space 3}0.379{col 84}{space 4}-.2260573{col 97}{space 3} .5935782
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .7300757{col 56}{space 2} .2071074{col 67}{space 1}    3.53{col 76}{space 3}0.000{col 84}{space 4} .3241416{col 97}{space 3}  1.13601
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .6104876{col 56}{space 2} .1963888{col 67}{space 1}    3.11{col 76}{space 3}0.002{col 84}{space 4} .2255622{col 97}{space 3}  .995413
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .4725558{col 56}{space 2}  .213758{col 67}{space 1}    2.21{col 76}{space 3}0.027{col 84}{space 4} .0535864{col 97}{space 3} .8915252
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} .6710963{col 56}{space 2}   .21166{col 67}{space 1}    3.17{col 76}{space 3}0.002{col 84}{space 4}  .256239{col 97}{space 3} 1.085954
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .5849207{col 56}{space 2} .1860869{col 67}{space 1}    3.14{col 76}{space 3}0.002{col 84}{space 4} .2201871{col 97}{space 3} .9496543
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .7018399{col 56}{space 2} .1931704{col 67}{space 1}    3.63{col 76}{space 3}0.000{col 84}{space 4} .3232226{col 97}{space 3} 1.080457
{txt}{space 37}2018  {c |}{col 44}{res}{space 2}  .568995{col 56}{space 2} .1941702{col 67}{space 1}    2.93{col 76}{space 3}0.003{col 84}{space 4}  .188418{col 97}{space 3}  .949572
{txt}{space 37}2019  {c |}{col 44}{res}{space 2} .1235327{col 56}{space 2} .1473356{col 67}{space 1}    0.84{col 76}{space 3}0.402{col 84}{space 4}-.1652477{col 97}{space 3} .4123131
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .2326619{col 56}{space 2} .1551295{col 67}{space 1}    1.50{col 76}{space 3}0.134{col 84}{space 4}-.0713947{col 97}{space 3} .5367185
{txt}{space 37}2021  {c |}{col 44}{res}{space 2} .0762717{col 56}{space 2} .1598471{col 67}{space 1}    0.48{col 76}{space 3}0.633{col 84}{space 4}-.2370315{col 97}{space 3} .3895749
{txt}{space 37}2022  {c |}{col 44}{res}{space 2} .1895093{col 56}{space 2} .1500701{col 67}{space 1}    1.26{col 76}{space 3}0.207{col 84}{space 4}-.1046308{col 97}{space 3} .4836494
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1189973{col 56}{space 2} .0288799{col 67}{space 1}   -4.12{col 76}{space 3}0.000{col 84}{space 4}-.1756023{col 97}{space 3}-.0623922
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0206847{col 56}{space 2} .0251701{col 67}{space 1}    0.82{col 76}{space 3}0.411{col 84}{space 4}-.0286491{col 97}{space 3} .0700184
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0621136{col 56}{space 2} .0174892{col 67}{space 1}    3.55{col 76}{space 3}0.000{col 84}{space 4} .0278345{col 97}{space 3} .0963927
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0496529{col 56}{space 2} .0209696{col 67}{space 1}    2.37{col 76}{space 3}0.018{col 84}{space 4} .0085522{col 97}{space 3} .0907536
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0031295{col 56}{space 2} .0209011{col 67}{space 1}    0.15{col 76}{space 3}0.881{col 84}{space 4}-.0378369{col 97}{space 3}  .044096
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} -.004673{col 56}{space 2} .0322865{col 67}{space 1}   -0.14{col 76}{space 3}0.885{col 84}{space 4} -.067955{col 97}{space 3} .0586091
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0377604{col 56}{space 2} .0208056{col 67}{space 1}    1.81{col 76}{space 3}0.070{col 84}{space 4} -.003019{col 97}{space 3} .0785397
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0547686{col 56}{space 2} .0194449{col 67}{space 1}   -2.82{col 76}{space 3}0.005{col 84}{space 4} -.092881{col 97}{space 3}-.0166562
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0443839{col 56}{space 2} .0281934{col 67}{space 1}    1.57{col 76}{space 3}0.115{col 84}{space 4}-.0108756{col 97}{space 3} .0996435
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .1059407{col 56}{space 2} .0248861{col 67}{space 1}    4.26{col 76}{space 3}0.000{col 84}{space 4} .0571635{col 97}{space 3} .1547178
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0075867{col 56}{space 2} .0192303{col 67}{space 1}    0.39{col 76}{space 3}0.693{col 84}{space 4} -.030105{col 97}{space 3} .0452785
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2}-.0077671{col 56}{space 2} .0191652{col 67}{space 1}   -0.41{col 76}{space 3}0.685{col 84}{space 4}-.0453313{col 97}{space 3}  .029797
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0258448{col 56}{space 2} .0212258{col 67}{space 1}    1.22{col 76}{space 3}0.223{col 84}{space 4}-.0157581{col 97}{space 3} .0674477
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2}  .037991{col 56}{space 2} .0250338{col 67}{space 1}    1.52{col 76}{space 3}0.129{col 84}{space 4}-.0110756{col 97}{space 3} .0870577
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0099762{col 56}{space 2} .0198066{col 67}{space 1}   -0.50{col 76}{space 3}0.614{col 84}{space 4}-.0487975{col 97}{space 3}  .028845
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0203855{col 56}{space 2}  .031725{col 67}{space 1}    0.64{col 76}{space 3}0.521{col 84}{space 4} -.041796{col 97}{space 3}  .082567
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1361831{col 56}{space 2} .0229083{col 67}{space 1}   -5.94{col 76}{space 3}0.000{col 84}{space 4}-.1810839{col 97}{space 3}-.0912824
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.1187632{col 56}{space 2} .0256365{col 67}{space 1}   -4.63{col 76}{space 3}0.000{col 84}{space 4}-.1690111{col 97}{space 3}-.0685152
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0570605{col 56}{space 2} .0200637{col 67}{space 1}    2.84{col 76}{space 3}0.004{col 84}{space 4} .0177352{col 97}{space 3} .0963858
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0742349{col 56}{space 2} .0270425{col 67}{space 1}    2.75{col 76}{space 3}0.006{col 84}{space 4} .0212312{col 97}{space 3} .1272386
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0447585{col 56}{space 2} .0271865{col 67}{space 1}    1.65{col 76}{space 3}0.100{col 84}{space 4}-.0085275{col 97}{space 3} .0980444
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0416922{col 56}{space 2}  .029974{col 67}{space 1}    1.39{col 76}{space 3}0.164{col 84}{space 4}-.0170573{col 97}{space 3} .1004418
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}-.0215089{col 56}{space 2}  .024751{col 67}{space 1}   -0.87{col 76}{space 3}0.385{col 84}{space 4}-.0700213{col 97}{space 3} .0270036
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0297567{col 56}{space 2} .0214377{col 67}{space 1}    1.39{col 76}{space 3}0.165{col 84}{space 4}-.0122615{col 97}{space 3} .0717749
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0570668{col 56}{space 2} .0225069{col 67}{space 1}    2.54{col 76}{space 3}0.011{col 84}{space 4} .0129529{col 97}{space 3} .1011806
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2}-.0087973{col 56}{space 2} .0228048{col 67}{space 1}   -0.39{col 76}{space 3}0.700{col 84}{space 4}-.0534951{col 97}{space 3} .0359006
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0565554{col 56}{space 2} .0245224{col 67}{space 1}    2.31{col 76}{space 3}0.021{col 84}{space 4}  .008491{col 97}{space 3} .1046198
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0601681{col 56}{space 2} .0240947{col 67}{space 1}   -2.50{col 76}{space 3}0.013{col 84}{space 4}-.1073941{col 97}{space 3}-.0129421
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0144935{col 56}{space 2} .0232772{col 67}{space 1}    0.62{col 76}{space 3}0.534{col 84}{space 4}-.0311301{col 97}{space 3} .0601172
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0072048{col 56}{space 2} .0167542{col 67}{space 1}    0.43{col 76}{space 3}0.667{col 84}{space 4}-.0256338{col 97}{space 3} .0400433
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0435247{col 56}{space 2} .0177602{col 67}{space 1}    2.45{col 76}{space 3}0.014{col 84}{space 4} .0087143{col 97}{space 3}  .078335
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0064037{col 56}{space 2} .0176352{col 67}{space 1}    0.36{col 76}{space 3}0.717{col 84}{space 4}-.0281615{col 97}{space 3}  .040969
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0475903{col 56}{space 2} .0229312{col 67}{space 1}   -2.08{col 76}{space 3}0.038{col 84}{space 4}-.0925358{col 97}{space 3}-.0026448
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2}  .005346{col 56}{space 2} .0251495{col 67}{space 1}    0.21{col 76}{space 3}0.832{col 84}{space 4}-.0439474{col 97}{space 3} .0546394
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0136478{col 56}{space 2} .0320777{col 67}{space 1}    0.43{col 76}{space 3}0.671{col 84}{space 4} -.049225{col 97}{space 3} .0765207
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0165143{col 56}{space 2} .0187699{col 67}{space 1}   -0.88{col 76}{space 3}0.379{col 84}{space 4}-.0533037{col 97}{space 3}  .020275
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0356769{col 56}{space 2} .0248709{col 67}{space 1}    1.43{col 76}{space 3}0.151{col 84}{space 4}-.0130705{col 97}{space 3} .0844242
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0548631{col 56}{space 2} .0194396{col 67}{space 1}    2.82{col 76}{space 3}0.005{col 84}{space 4} .0167611{col 97}{space 3}  .092965
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .1087899{col 56}{space 2} .0432434{col 67}{space 1}    2.52{col 76}{space 3}0.012{col 84}{space 4} .0240321{col 97}{space 3} .1935477
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0103474{col 56}{space 2} .0194025{col 67}{space 1}    0.53{col 76}{space 3}0.594{col 84}{space 4}-.0276817{col 97}{space 3} .0483766
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .1137406{col 56}{space 2} .0714638{col 67}{space 1}    1.59{col 76}{space 3}0.111{col 84}{space 4}-.0263297{col 97}{space 3} .2538108
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0704319{col 56}{space 2} .0251218{col 67}{space 1}   -2.80{col 76}{space 3}0.005{col 84}{space 4} -.119671{col 97}{space 3}-.0211927
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0001985{col 56}{space 2} .0341855{col 67}{space 1}   -0.01{col 76}{space 3}0.995{col 84}{space 4}-.0672025{col 97}{space 3} .0668056
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2}  .050219{col 56}{space 2} .0209713{col 67}{space 1}    2.39{col 76}{space 3}0.017{col 84}{space 4} .0091149{col 97}{space 3} .0913231
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0250297{col 56}{space 2} .0159246{col 67}{space 1}    1.57{col 76}{space 3}0.116{col 84}{space 4}-.0061828{col 97}{space 3} .0562421
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}-.0056353{col 56}{space 2} .0203461{col 67}{space 1}   -0.28{col 76}{space 3}0.782{col 84}{space 4} -.045514{col 97}{space 3} .0342434
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.0970129{col 56}{space 2} .0175764{col 67}{space 1}   -5.52{col 76}{space 3}0.000{col 84}{space 4}-.1314629{col 97}{space 3} -.062563
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .1120937{col 56}{space 2} .0332453{col 67}{space 1}    3.37{col 76}{space 3}0.001{col 84}{space 4} .0469323{col 97}{space 3} .1772551
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0170048{col 56}{space 2} .0193752{col 67}{space 1}    0.88{col 76}{space 3}0.380{col 84}{space 4}-.0209709{col 97}{space 3} .0549806
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0062779{col 56}{space 2} .0086845{col 67}{space 1}    0.72{col 76}{space 3}0.470{col 84}{space 4}-.0107439{col 97}{space 3} .0232996
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0024162{col 56}{space 2} .0083476{col 67}{space 1}   -0.29{col 76}{space 3}0.772{col 84}{space 4}-.0187776{col 97}{space 3} .0139452
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0061706{col 56}{space 2} .0094101{col 67}{space 1}    0.66{col 76}{space 3}0.512{col 84}{space 4}-.0122733{col 97}{space 3} .0246145
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0140696{col 56}{space 2} .0084789{col 67}{space 1}    1.66{col 76}{space 3}0.097{col 84}{space 4}-.0025492{col 97}{space 3} .0306884
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0132169{col 56}{space 2} .0121681{col 67}{space 1}   -1.09{col 76}{space 3}0.277{col 84}{space 4}-.0370665{col 97}{space 3} .0106328
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0127976{col 56}{space 2} .0116488{col 67}{space 1}    1.10{col 76}{space 3}0.272{col 84}{space 4}-.0100342{col 97}{space 3} .0356294
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0023925{col 56}{space 2} .0041838{col 67}{space 1}    0.57{col 76}{space 3}0.567{col 84}{space 4}-.0058078{col 97}{space 3} .0105928
{txt}{space 39}age {c |}{col 44}{res}{space 2}  .001597{col 56}{space 2} .0002658{col 67}{space 1}    6.01{col 76}{space 3}0.000{col 84}{space 4} .0010761{col 97}{space 3}  .002118
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.031309{col 56}{space 2} .0052315{col 67}{space 1}   -5.98{col 76}{space 3}0.000{col 84}{space 4}-.0415628{col 97}{space 3}-.0210552
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0524048{col 56}{space 2}  .005612{col 67}{space 1}   -9.34{col 76}{space 3}0.000{col 84}{space 4}-.0634044{col 97}{space 3}-.0414052
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0079774{col 56}{space 2} .0070715{col 67}{space 1}    1.13{col 76}{space 3}0.259{col 84}{space 4}-.0058829{col 97}{space 3} .0218377
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0383679{col 56}{space 2} .0065266{col 67}{space 1}    5.88{col 76}{space 3}0.000{col 84}{space 4} .0255755{col 97}{space 3} .0511602
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.1233744{col 56}{space 2} .1082444{col 67}{space 1}   -1.14{col 76}{space 3}0.254{col 84}{space 4}-.3355354{col 97}{space 3} .0887865
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0180253{col 56}{space 2} .0085924{col 67}{space 1}    2.10{col 76}{space 3}0.036{col 84}{space 4}  .001184{col 97}{space 3} .0348666
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1931908{col 56}{space 2} .0384283{col 67}{space 1}    5.03{col 76}{space 3}0.000{col 84}{space 4} .1178706{col 97}{space 3} .2685111
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}44320{txt}) = {res}7.475{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.5865
{col 25}{txt}Prob>|t| = {res}    0.5626

95%{txt} confidence set for null hypothesis expression: {res}[−.02027, .009222]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:44,418}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2358053{col 28}{space 2} .0020142{col 39}{space 5} .2318574{col 53}{space 3} .2397532
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#0b.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#1.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#3.education} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:113,372}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:56}
{txt}{col 52}{lalign 17:F({res:163}, {res:113153})}{col 69} = {res}{ralign 7:19.53}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0381}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0363}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4389}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2} .0013609{col 58}{space 2} .0045235{col 69}{space 1}    0.30{col 78}{space 3}0.764{col 86}{space 4} -.007505{col 99}{space 3} .0102268
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2} .0345176{col 58}{space 2} .1822951{col 69}{space 1}    0.19{col 78}{space 3}0.850{col 86}{space 4}-.3227781{col 99}{space 3} .3918133
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}-.1647363{col 58}{space 2} .2539832{col 69}{space 1}   -0.65{col 78}{space 3}0.517{col 86}{space 4}-.6625396{col 99}{space 3} .3330671
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.1144745{col 58}{space 2} .2091516{col 69}{space 1}   -0.55{col 78}{space 3}0.584{col 86}{space 4}-.5244085{col 99}{space 3} .2954594
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.2801181{col 58}{space 2} .2094084{col 69}{space 1}   -1.34{col 78}{space 3}0.181{col 86}{space 4}-.6905554{col 99}{space 3} .1303192
{txt}{space 39}1991  {c |}{col 46}{res}{space 2} -.164529{col 58}{space 2}  .258528{col 69}{space 1}   -0.64{col 78}{space 3}0.525{col 86}{space 4}  -.67124{col 99}{space 3}  .342182
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}-.3323021{col 58}{space 2} .2177714{col 69}{space 1}   -1.53{col 78}{space 3}0.127{col 86}{space 4}-.7591308{col 99}{space 3} .0945265
{txt}{space 39}1993  {c |}{col 46}{res}{space 2}-.0251545{col 58}{space 2} .2042155{col 69}{space 1}   -0.12{col 78}{space 3}0.902{col 86}{space 4}-.4254139{col 99}{space 3} .3751048
{txt}{space 39}1994  {c |}{col 46}{res}{space 2}-.0926441{col 58}{space 2} .2077728{col 69}{space 1}   -0.45{col 78}{space 3}0.656{col 86}{space 4}-.4998758{col 99}{space 3} .3145875
{txt}{space 39}1995  {c |}{col 46}{res}{space 2}-.1784456{col 58}{space 2} .2027541{col 69}{space 1}   -0.88{col 78}{space 3}0.379{col 86}{space 4}-.5758406{col 99}{space 3} .2189493
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}-.0316992{col 58}{space 2} .2198556{col 69}{space 1}   -0.14{col 78}{space 3}0.885{col 86}{space 4}-.4626128{col 99}{space 3} .3992144
{txt}{space 39}1997  {c |}{col 46}{res}{space 2} .5898955{col 58}{space 2}  .246984{col 69}{space 1}    2.39{col 78}{space 3}0.017{col 86}{space 4} .1058105{col 99}{space 3} 1.073981
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .0887692{col 58}{space 2} .2638183{col 69}{space 1}    0.34{col 78}{space 3}0.737{col 86}{space 4}-.4283108{col 99}{space 3} .6058491
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .1168467{col 58}{space 2} .2576823{col 69}{space 1}    0.45{col 78}{space 3}0.650{col 86}{space 4}-.3882068{col 99}{space 3} .6219001
{txt}{space 39}2000  {c |}{col 46}{res}{space 2} .4871276{col 58}{space 2} .1844295{col 69}{space 1}    2.64{col 78}{space 3}0.008{col 86}{space 4} .1256485{col 99}{space 3} .8486068
{txt}{space 39}2001  {c |}{col 46}{res}{space 2} .4756567{col 58}{space 2} .2426502{col 69}{space 1}    1.96{col 78}{space 3}0.050{col 86}{space 4}  .000066{col 99}{space 3} .9512473
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .2262689{col 58}{space 2} .2463342{col 69}{space 1}    0.92{col 78}{space 3}0.358{col 86}{space 4}-.2565424{col 99}{space 3} .7090803
{txt}{space 39}2003  {c |}{col 46}{res}{space 2} .2330351{col 58}{space 2} .2335587{col 69}{space 1}    1.00{col 78}{space 3}0.318{col 86}{space 4}-.2247364{col 99}{space 3} .6908067
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} .4226277{col 58}{space 2} .1833272{col 69}{space 1}    2.31{col 78}{space 3}0.021{col 86}{space 4} .0633092{col 99}{space 3} .7819462
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}-.0613289{col 58}{space 2} .2388907{col 69}{space 1}   -0.26{col 78}{space 3}0.797{col 86}{space 4} -.529551{col 99}{space 3} .4068932
{txt}{space 39}2006  {c |}{col 46}{res}{space 2}-.3319554{col 58}{space 2} .2233688{col 69}{space 1}   -1.49{col 78}{space 3}0.137{col 86}{space 4}-.7697548{col 99}{space 3} .1058441
{txt}{space 39}2007  {c |}{col 46}{res}{space 2}-.1695608{col 58}{space 2} .2152888{col 69}{space 1}   -0.79{col 78}{space 3}0.431{col 86}{space 4}-.5915236{col 99}{space 3}  .252402
{txt}{space 39}2008  {c |}{col 46}{res}{space 2}-.0421775{col 58}{space 2} .1824206{col 69}{space 1}   -0.23{col 78}{space 3}0.817{col 86}{space 4}-.3997192{col 99}{space 3} .3153642
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.3506582{col 58}{space 2} .2226856{col 69}{space 1}   -1.57{col 78}{space 3}0.115{col 86}{space 4}-.7871185{col 99}{space 3} .0858021
{txt}{space 39}2010  {c |}{col 46}{res}{space 2} -.511324{col 58}{space 2} .2544722{col 69}{space 1}   -2.01{col 78}{space 3}0.045{col 86}{space 4}-1.010086{col 99}{space 3}-.0125622
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.3289309{col 58}{space 2} .2699026{col 69}{space 1}   -1.22{col 78}{space 3}0.223{col 86}{space 4} -.857936{col 99}{space 3} .2000742
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .2155271{col 58}{space 2} .2673032{col 69}{space 1}    0.81{col 78}{space 3}0.420{col 86}{space 4}-.3083832{col 99}{space 3} .7394373
{txt}{space 39}2013  {c |}{col 46}{res}{space 2} .0943719{col 58}{space 2} .2595677{col 69}{space 1}    0.36{col 78}{space 3}0.716{col 86}{space 4}-.4143769{col 99}{space 3} .6031206
{txt}{space 39}2014  {c |}{col 46}{res}{space 2}-.0373388{col 58}{space 2} .2775699{col 69}{space 1}   -0.13{col 78}{space 3}0.893{col 86}{space 4}-.5813717{col 99}{space 3} .5066941
{txt}{space 39}2015  {c |}{col 46}{res}{space 2} .1611067{col 58}{space 2} .2746549{col 69}{space 1}    0.59{col 78}{space 3}0.557{col 86}{space 4}-.3772127{col 99}{space 3} .6994261
{txt}{space 39}2016  {c |}{col 46}{res}{space 2} .0725159{col 58}{space 2} .2520315{col 69}{space 1}    0.29{col 78}{space 3}0.774{col 86}{space 4}-.4214621{col 99}{space 3} .5664939
{txt}{space 39}2017  {c |}{col 46}{res}{space 2} .1903184{col 58}{space 2} .2582069{col 69}{space 1}    0.74{col 78}{space 3}0.461{col 86}{space 4}-.3157632{col 99}{space 3}    .6964
{txt}{space 39}2018  {c |}{col 46}{res}{space 2} .0531415{col 58}{space 2} .2597331{col 69}{space 1}    0.20{col 78}{space 3}0.838{col 86}{space 4}-.4559314{col 99}{space 3} .5622144
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.3933055{col 58}{space 2} .2245141{col 69}{space 1}   -1.75{col 78}{space 3}0.080{col 86}{space 4}-.8333498{col 99}{space 3} .0467387
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}-.2781625{col 58}{space 2} .2293257{col 69}{space 1}   -1.21{col 78}{space 3}0.225{col 86}{space 4}-.7276374{col 99}{space 3} .1713124
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.4342752{col 58}{space 2} .2376544{col 69}{space 1}   -1.83{col 78}{space 3}0.068{col 86}{space 4}-.9000741{col 99}{space 3} .0315238
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.3222299{col 58}{space 2} .2167663{col 69}{space 1}   -1.49{col 78}{space 3}0.137{col 86}{space 4}-.7470885{col 99}{space 3} .1026287
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.1187186{col 58}{space 2} .0302772{col 69}{space 1}   -3.92{col 78}{space 3}0.000{col 86}{space 4}-.1780614{col 99}{space 3}-.0593757
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2} .0158538{col 58}{space 2} .0257734{col 69}{space 1}    0.62{col 78}{space 3}0.538{col 86}{space 4}-.0346616{col 99}{space 3} .0663692
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0615695{col 58}{space 2} .0183254{col 69}{space 1}    3.36{col 78}{space 3}0.001{col 86}{space 4} .0256521{col 99}{space 3}  .097487
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0490145{col 58}{space 2} .0219727{col 69}{space 1}    2.23{col 78}{space 3}0.026{col 86}{space 4} .0059484{col 99}{space 3} .0920806
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2} .0029584{col 58}{space 2} .0219127{col 69}{space 1}    0.14{col 78}{space 3}0.893{col 86}{space 4}-.0399902{col 99}{space 3} .0459069
{txt}{space 38}avila  {c |}{col 46}{res}{space 2}-.0047097{col 58}{space 2} .0338506{col 69}{space 1}   -0.14{col 78}{space 3}0.889{col 86}{space 4}-.0710563{col 99}{space 3} .0616369
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0378204{col 58}{space 2} .0218134{col 69}{space 1}    1.73{col 78}{space 3}0.083{col 86}{space 4}-.0049336{col 99}{space 3} .0805744
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.0544834{col 58}{space 2} .0203842{col 69}{space 1}   -2.67{col 78}{space 3}0.008{col 86}{space 4}-.0944362{col 99}{space 3}-.0145307
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0435339{col 58}{space 2} .0295424{col 69}{space 1}    1.47{col 78}{space 3}0.141{col 86}{space 4}-.0143687{col 99}{space 3} .1014366
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2} .1058608{col 58}{space 2} .0260915{col 69}{space 1}    4.06{col 78}{space 3}0.000{col 86}{space 4} .0547218{col 99}{space 3} .1569998
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2} .0075804{col 58}{space 2} .0201619{col 69}{space 1}    0.38{col 78}{space 3}0.707{col 86}{space 4}-.0319367{col 99}{space 3} .0470975
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2}-.0079236{col 58}{space 2} .0200928{col 69}{space 1}   -0.39{col 78}{space 3}0.693{col 86}{space 4}-.0473053{col 99}{space 3}  .031458
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2} .0258822{col 58}{space 2}  .022254{col 69}{space 1}    1.16{col 78}{space 3}0.245{col 86}{space 4}-.0177354{col 99}{space 3} .0694997
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0379509{col 58}{space 2} .0262465{col 69}{space 1}    1.45{col 78}{space 3}0.148{col 86}{space 4}-.0134918{col 99}{space 3} .0893936
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2}-.0099299{col 58}{space 2}  .020766{col 69}{space 1}   -0.48{col 78}{space 3}0.633{col 86}{space 4}-.0506311{col 99}{space 3} .0307712
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2} .0153168{col 58}{space 2} .0327258{col 69}{space 1}    0.47{col 78}{space 3}0.640{col 86}{space 4}-.0488252{col 99}{space 3} .0794588
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.1359356{col 58}{space 2} .0240164{col 69}{space 1}   -5.66{col 78}{space 3}0.000{col 86}{space 4}-.1830073{col 99}{space 3}-.0888638
{txt}{space 37}girona  {c |}{col 46}{res}{space 2}-.1183987{col 58}{space 2} .0268751{col 69}{space 1}   -4.41{col 78}{space 3}0.000{col 86}{space 4}-.1710734{col 99}{space 3} -.065724
{txt}{space 36}granada  {c |}{col 46}{res}{space 2} .0563738{col 58}{space 2} .0210203{col 69}{space 1}    2.68{col 78}{space 3}0.007{col 86}{space 4} .0151743{col 99}{space 3} .0975733
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0743902{col 58}{space 2}  .028352{col 69}{space 1}    2.62{col 78}{space 3}0.009{col 86}{space 4} .0188207{col 99}{space 3} .1299596
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2} .0438104{col 58}{space 2} .0284818{col 69}{space 1}    1.54{col 78}{space 3}0.124{col 86}{space 4}-.0120135{col 99}{space 3} .0996343
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2} .0365729{col 58}{space 2} .0308466{col 69}{space 1}    1.19{col 78}{space 3}0.236{col 86}{space 4}-.0238859{col 99}{space 3} .0970318
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2}-.0211049{col 58}{space 2} .0259458{col 69}{space 1}   -0.81{col 78}{space 3}0.416{col 86}{space 4}-.0719582{col 99}{space 3} .0297485
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0297256{col 58}{space 2} .0224762{col 69}{space 1}    1.32{col 78}{space 3}0.186{col 86}{space 4}-.0143274{col 99}{space 3} .0737786
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0569124{col 58}{space 2} .0235965{col 69}{space 1}    2.41{col 78}{space 3}0.016{col 86}{space 4} .0106635{col 99}{space 3} .1031613
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} -.009198{col 58}{space 2}  .023905{col 69}{space 1}   -0.38{col 78}{space 3}0.700{col 86}{space 4}-.0560514{col 99}{space 3} .0376554
{txt}{space 39}leon  {c |}{col 46}{res}{space 2} .0564567{col 58}{space 2} .0257102{col 69}{space 1}    2.20{col 78}{space 3}0.028{col 86}{space 4} .0060651{col 99}{space 3} .1068482
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2}-.0604829{col 58}{space 2} .0252593{col 69}{space 1}   -2.39{col 78}{space 3}0.017{col 86}{space 4}-.1099907{col 99}{space 3}-.0109751
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2} .0129357{col 58}{space 2} .0243363{col 69}{space 1}    0.53{col 78}{space 3}0.595{col 86}{space 4} -.034763{col 99}{space 3} .0606345
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2} .0069758{col 58}{space 2} .0175638{col 69}{space 1}    0.40{col 78}{space 3}0.691{col 86}{space 4} -.027449{col 99}{space 3} .0414006
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0435444{col 58}{space 2} .0186206{col 69}{space 1}    2.34{col 78}{space 3}0.019{col 86}{space 4} .0070483{col 99}{space 3} .0800405
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2} .0064455{col 58}{space 2} .0184895{col 69}{space 1}    0.35{col 78}{space 3}0.727{col 86}{space 4}-.0297936{col 99}{space 3} .0426846
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0473524{col 58}{space 2} .0240405{col 69}{space 1}   -1.97{col 78}{space 3}0.049{col 86}{space 4}-.0944714{col 99}{space 3}-.0002335
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} .0055457{col 58}{space 2} .0263668{col 69}{space 1}    0.21{col 78}{space 3}0.833{col 86}{space 4}-.0461329{col 99}{space 3} .0572242
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2}  .013622{col 58}{space 2} .0336317{col 69}{space 1}    0.41{col 78}{space 3}0.685{col 86}{space 4}-.0522956{col 99}{space 3} .0795397
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2}-.0164047{col 58}{space 2} .0196788{col 69}{space 1}   -0.83{col 78}{space 3}0.404{col 86}{space 4}-.0549749{col 99}{space 3} .0221655
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2} .0347923{col 58}{space 2} .0260551{col 69}{space 1}    1.34{col 78}{space 3}0.182{col 86}{space 4}-.0162753{col 99}{space 3} .0858598
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .0547362{col 58}{space 2} .0203808{col 69}{space 1}    2.69{col 78}{space 3}0.007{col 86}{space 4} .0147901{col 99}{space 3} .0946823
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2} .1088294{col 58}{space 2} .0453383{col 69}{space 1}    2.40{col 78}{space 3}0.016{col 86}{space 4}  .019967{col 99}{space 3} .1976919
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2} .0105396{col 58}{space 2} .0203412{col 69}{space 1}    0.52{col 78}{space 3}0.604{col 86}{space 4}-.0293288{col 99}{space 3}  .050408
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .1136514{col 58}{space 2} .0749258{col 69}{space 1}    1.52{col 78}{space 3}0.129{col 86}{space 4} -.033202{col 99}{space 3} .2605049
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2}-.0718171{col 58}{space 2} .0262886{col 69}{space 1}   -2.73{col 78}{space 3}0.006{col 86}{space 4}-.1233424{col 99}{space 3}-.0202918
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2}-5.30e-06{col 58}{space 2} .0358409{col 69}{space 1}   -0.00{col 78}{space 3}1.000{col 86}{space 4}-.0702529{col 99}{space 3} .0702423
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2}  .050138{col 58}{space 2} .0219871{col 69}{space 1}    2.28{col 78}{space 3}0.023{col 86}{space 4} .0070437{col 99}{space 3} .0932323
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2} .0248129{col 58}{space 2} .0166941{col 69}{space 1}    1.49{col 78}{space 3}0.137{col 86}{space 4}-.0079074{col 99}{space 3} .0575331
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2}-.0055081{col 58}{space 2} .0213313{col 69}{space 1}   -0.26{col 78}{space 3}0.796{col 86}{space 4} -.047317{col 99}{space 3} .0363009
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.0982749{col 58}{space 2} .0183683{col 69}{space 1}   -5.35{col 78}{space 3}0.000{col 86}{space 4}-.1342764{col 99}{space 3}-.0622733
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2} .1112274{col 58}{space 2} .0348411{col 69}{space 1}    3.19{col 78}{space 3}0.001{col 86}{space 4} .0429395{col 99}{space 3} .1795154
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2} .0170678{col 58}{space 2} .0203137{col 69}{space 1}    0.84{col 78}{space 3}0.401{col 86}{space 4}-.0227467{col 99}{space 3} .0568824
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0063323{col 58}{space 2}  .009105{col 69}{space 1}    0.70{col 78}{space 3}0.487{col 86}{space 4}-.0115134{col 99}{space 3} .0241779
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} -.002385{col 58}{space 2} .0087519{col 69}{space 1}   -0.27{col 78}{space 3}0.785{col 86}{space 4}-.0195386{col 99}{space 3} .0147687
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0061937{col 58}{space 2} .0098659{col 69}{space 1}    0.63{col 78}{space 3}0.530{col 86}{space 4}-.0131434{col 99}{space 3} .0255307
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0141055{col 58}{space 2} .0088896{col 69}{space 1}    1.59{col 78}{space 3}0.113{col 86}{space 4}-.0033179{col 99}{space 3}  .031529
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0131849{col 58}{space 2} .0127575{col 69}{space 1}   -1.03{col 78}{space 3}0.301{col 86}{space 4}-.0381894{col 99}{space 3} .0118196
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2} .0128356{col 58}{space 2}  .012213{col 69}{space 1}    1.05{col 78}{space 3}0.293{col 86}{space 4}-.0111018{col 99}{space 3} .0367729
{txt}{space 44} {c |}
{space 38}female {c |}{col 46}{res}{space 2}-.0276613{col 58}{space 2} .0040718{col 69}{space 1}   -6.79{col 78}{space 3}0.000{col 86}{space 4}-.0356419{col 99}{space 3}-.0196807
{txt}{space 41}age {c |}{col 46}{res}{space 2} .0015983{col 58}{space 2} .0002786{col 69}{space 1}    5.74{col 78}{space 3}0.000{col 86}{space 4} .0010522{col 99}{space 3} .0021445
{txt}{space 44} {c |}
{space 35}education {c |}
{space 34}Secondary  {c |}{col 46}{res}{space 2}-.0312964{col 58}{space 2} .0054849{col 69}{space 1}   -5.71{col 78}{space 3}0.000{col 86}{space 4}-.0420467{col 99}{space 3}-.0205461
{txt}{space 27}Higher Education  {c |}{col 46}{res}{space 2}-.0524022{col 58}{space 2} .0058839{col 69}{space 1}   -8.91{col 78}{space 3}0.000{col 86}{space 4}-.0639345{col 99}{space 3}-.0408699
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0080614{col 58}{space 2} .0074135{col 69}{space 1}    1.09{col 78}{space 3}0.277{col 86}{space 4}-.0064689{col 99}{space 3} .0225917
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0383505{col 58}{space 2} .0068428{col 69}{space 1}    5.60{col 78}{space 3}0.000{col 86}{space 4} .0249387{col 99}{space 3} .0517622
{txt}{space 36}Student  {c |}{col 46}{res}{space 2}-.1239593{col 58}{space 2} .1134863{col 69}{space 1}   -1.09{col 78}{space 3}0.275{col 86}{space 4}-.3463908{col 99}{space 3} .0984721
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2} .0180058{col 58}{space 2} .0090086{col 69}{space 1}    2.00{col 78}{space 3}0.046{col 86}{space 4}  .000349{col 99}{space 3} .0356626
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2}  .048142{col 58}{space 2} .0158271{col 69}{space 1}    3.04{col 78}{space 3}0.002{col 86}{space 4} .0171211{col 99}{space 3} .0791628
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0373231{col 58}{space 2} .0143645{col 69}{space 1}    2.60{col 78}{space 3}0.009{col 86}{space 4} .0091688{col 99}{space 3} .0654773
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0430167{col 58}{space 2} .0136444{col 69}{space 1}    3.15{col 78}{space 3}0.002{col 86}{space 4}  .016274{col 99}{space 3} .0697595
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0125428{col 58}{space 2}  .014612{col 69}{space 1}    0.86{col 78}{space 3}0.391{col 86}{space 4}-.0160965{col 99}{space 3}  .041182
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0303652{col 58}{space 2} .0133371{col 69}{space 1}    2.28{col 78}{space 3}0.023{col 86}{space 4} .0042248{col 99}{space 3} .0565056
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}  .033082{col 58}{space 2} .0188953{col 69}{space 1}    1.75{col 78}{space 3}0.080{col 86}{space 4}-.0039525{col 99}{space 3} .0701164
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#female {c |}
{space 40}0 1  {c |}{col 46}{res}{space 2} .0300642{col 58}{space 2}  .005985{col 69}{space 1}    5.02{col 78}{space 3}0.000{col 86}{space 4} .0183336{col 99}{space 3} .0417947
{txt}{space 40}1 0  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 40}1 1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2}-.0009447{col 58}{space 2} .0003478{col 69}{space 1}   -2.72{col 78}{space 3}0.007{col 86}{space 4}-.0016263{col 99}{space 3} -.000263
{txt}{space 44} {c |}
{space 25}preperiod#education {c |}
{space 19}1#Primary School or less  {c |}{col 46}{res}{space 2}-.0361093{col 58}{space 2}  .008792{col 69}{space 1}   -4.11{col 78}{space 3}0.000{col 86}{space 4}-.0533415{col 99}{space 3}-.0188772
{txt}{space 32}1#Secondary  {c |}{col 46}{res}{space 2} -.006467{col 58}{space 2} .0098723{col 69}{space 1}   -0.66{col 78}{space 3}0.512{col 86}{space 4}-.0258165{col 99}{space 3} .0128825
{txt}{space 25}1#Higher Education  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2} .0105237{col 58}{space 2} .0106661{col 69}{space 1}    0.99{col 78}{space 3}0.324{col 86}{space 4}-.0103818{col 99}{space 3} .0314291
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2}-.0025811{col 58}{space 2} .0136066{col 69}{space 1}   -0.19{col 78}{space 3}0.850{col 86}{space 4}-.0292498{col 99}{space 3} .0240877
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2}-.0066281{col 58}{space 2}  .009953{col 69}{space 1}   -0.67{col 78}{space 3}0.505{col 86}{space 4}-.0261359{col 99}{space 3} .0128797
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2} .1587999{col 58}{space 2} .1157022{col 69}{space 1}    1.37{col 78}{space 3}0.170{col 86}{space 4}-.0679747{col 99}{space 3} .3855744
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2} .0031148{col 58}{space 2} .0262032{col 69}{space 1}    0.12{col 78}{space 3}0.905{col 86}{space 4}-.0482431{col 99}{space 3} .0544726
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2} .0399512{col 58}{space 2} .0372208{col 69}{space 1}    1.07{col 78}{space 3}0.283{col 86}{space 4} -.033001{col 99}{space 3} .1129034
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .0581301{col 58}{space 2} .0340581{col 69}{space 1}    1.71{col 78}{space 3}0.088{col 86}{space 4}-.0086232{col 99}{space 3} .1248834
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0602904{col 58}{space 2} .0268572{col 69}{space 1}    2.24{col 78}{space 3}0.025{col 86}{space 4} .0076507{col 99}{space 3}   .11293
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0331323{col 58}{space 2} .0312461{col 69}{space 1}    1.06{col 78}{space 3}0.289{col 86}{space 4}-.0281095{col 99}{space 3} .0943742
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2} .0118194{col 58}{space 2} .0297781{col 69}{space 1}    0.40{col 78}{space 3}0.691{col 86}{space 4}-.0465452{col 99}{space 3} .0701841
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0903343{col 58}{space 2} .0421462{col 69}{space 1}    2.14{col 78}{space 3}0.032{col 86}{space 4} .0077285{col 99}{space 3} .1729401
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0961401{col 58}{space 2} .0301524{col 69}{space 1}    3.19{col 78}{space 3}0.001{col 86}{space 4} .0370418{col 99}{space 3} .1552385
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} .0473763{col 58}{space 2} .0260012{col 69}{space 1}    1.82{col 78}{space 3}0.068{col 86}{space 4}-.0035857{col 99}{space 3} .0983383
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0233248{col 58}{space 2}  .037412{col 69}{space 1}   -0.62{col 78}{space 3}0.533{col 86}{space 4}-.0966516{col 99}{space 3} .0500021
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2} .0026074{col 58}{space 2} .0339789{col 69}{space 1}    0.08{col 78}{space 3}0.939{col 86}{space 4}-.0639907{col 99}{space 3} .0692055
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .0612307{col 58}{space 2} .0293502{col 69}{space 1}    2.09{col 78}{space 3}0.037{col 86}{space 4} .0037047{col 99}{space 3} .1187567
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .1245082{col 58}{space 2} .0293941{col 69}{space 1}    4.24{col 78}{space 3}0.000{col 86}{space 4} .0668963{col 99}{space 3} .1821201
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .0841428{col 58}{space 2} .0305498{col 69}{space 1}    2.75{col 78}{space 3}0.006{col 86}{space 4} .0242657{col 99}{space 3} .1440199
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2} .0373583{col 58}{space 2} .0342292{col 69}{space 1}    1.09{col 78}{space 3}0.275{col 86}{space 4}-.0297304{col 99}{space 3} .1044471
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2}-.0093959{col 58}{space 2} .0290925{col 69}{space 1}   -0.32{col 78}{space 3}0.747{col 86}{space 4}-.0664167{col 99}{space 3} .0476249
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2}  .062361{col 58}{space 2} .0407226{col 69}{space 1}    1.53{col 78}{space 3}0.126{col 86}{space 4}-.0174546{col 99}{space 3} .1421766
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0277961{col 58}{space 2} .0307028{col 69}{space 1}   -0.91{col 78}{space 3}0.365{col 86}{space 4}-.0879732{col 99}{space 3}  .032381
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2}  .054363{col 58}{space 2} .0337531{col 69}{space 1}    1.61{col 78}{space 3}0.107{col 86}{space 4}-.0117926{col 99}{space 3} .1205186
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2}-.0036494{col 58}{space 2} .0296882{col 69}{space 1}   -0.12{col 78}{space 3}0.902{col 86}{space 4}-.0618378{col 99}{space 3} .0545389
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2} .0004584{col 58}{space 2}  .037235{col 69}{space 1}    0.01{col 78}{space 3}0.990{col 86}{space 4}-.0725216{col 99}{space 3} .0734384
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} .0420767{col 58}{space 2} .0367215{col 69}{space 1}    1.15{col 78}{space 3}0.252{col 86}{space 4}-.0298969{col 99}{space 3} .1140503
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2}-.0365981{col 58}{space 2} .0388761{col 69}{space 1}   -0.94{col 78}{space 3}0.346{col 86}{space 4}-.1127947{col 99}{space 3} .0395984
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .0848958{col 58}{space 2} .0321543{col 69}{space 1}    2.64{col 78}{space 3}0.008{col 86}{space 4} .0218738{col 99}{space 3} .1479177
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2} .0210346{col 58}{space 2} .0308332{col 69}{space 1}    0.68{col 78}{space 3}0.495{col 86}{space 4}-.0393981{col 99}{space 3} .0814673
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0497074{col 58}{space 2} .0333505{col 69}{space 1}   -1.49{col 78}{space 3}0.136{col 86}{space 4} -.115074{col 99}{space 3} .0156591
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .1147791{col 58}{space 2} .0319414{col 69}{space 1}    3.59{col 78}{space 3}0.000{col 86}{space 4} .0521745{col 99}{space 3} .1773838
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2}-.0371487{col 58}{space 2} .0336027{col 69}{space 1}   -1.11{col 78}{space 3}0.269{col 86}{space 4}-.1030095{col 99}{space 3} .0287121
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2}-.0399901{col 58}{space 2} .0346324{col 69}{space 1}   -1.15{col 78}{space 3}0.248{col 86}{space 4}-.1078691{col 99}{space 3}  .027889
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0643845{col 58}{space 2} .0325627{col 69}{space 1}    1.98{col 78}{space 3}0.048{col 86}{space 4} .0005622{col 99}{space 3} .1282069
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0440253{col 58}{space 2} .0244764{col 69}{space 1}    1.80{col 78}{space 3}0.072{col 86}{space 4} -.003948{col 99}{space 3} .0919987
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .0614019{col 58}{space 2} .0249196{col 69}{space 1}    2.46{col 78}{space 3}0.014{col 86}{space 4} .0125598{col 99}{space 3}  .110244
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2}   .05864{col 58}{space 2} .0258545{col 69}{space 1}    2.27{col 78}{space 3}0.023{col 86}{space 4} .0079654{col 99}{space 3} .1093145
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2} .0391917{col 58}{space 2} .0316064{col 69}{space 1}    1.24{col 78}{space 3}0.215{col 86}{space 4}-.0227563{col 99}{space 3} .1011397
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2} .0803013{col 58}{space 2} .0335049{col 69}{space 1}    2.40{col 78}{space 3}0.017{col 86}{space 4} .0146321{col 99}{space 3} .1459704
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2} .0306214{col 58}{space 2} .0422815{col 69}{space 1}    0.72{col 78}{space 3}0.469{col 86}{space 4}-.0522496{col 99}{space 3} .1134925
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}   .00884{col 58}{space 2} .0275219{col 69}{space 1}    0.32{col 78}{space 3}0.748{col 86}{space 4}-.0451026{col 99}{space 3} .0627825
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2}  .094101{col 58}{space 2} .0343121{col 69}{space 1}    2.74{col 78}{space 3}0.006{col 86}{space 4} .0268499{col 99}{space 3} .1613522
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0207609{col 58}{space 2} .0293205{col 69}{space 1}   -0.71{col 78}{space 3}0.479{col 86}{space 4}-.0782286{col 99}{space 3} .0367067
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0887003{col 58}{space 2} .0527086{col 69}{space 1}   -1.68{col 78}{space 3}0.092{col 86}{space 4}-.1920085{col 99}{space 3} .0146078
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .0759859{col 58}{space 2} .0256678{col 69}{space 1}    2.96{col 78}{space 3}0.003{col 86}{space 4} .0256774{col 99}{space 3} .1262945
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2}-.0572724{col 58}{space 2} .0814747{col 69}{space 1}   -0.70{col 78}{space 3}0.482{col 86}{space 4}-.2169615{col 99}{space 3} .1024167
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2} .0807413{col 58}{space 2} .0332117{col 69}{space 1}    2.43{col 78}{space 3}0.015{col 86}{space 4} .0156469{col 99}{space 3} .1458357
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0463286{col 58}{space 2} .0426151{col 69}{space 1}    1.09{col 78}{space 3}0.277{col 86}{space 4}-.0371965{col 99}{space 3} .1298536
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2} .0138717{col 58}{space 2} .0308584{col 69}{space 1}    0.45{col 78}{space 3}0.653{col 86}{space 4}-.0466104{col 99}{space 3} .0743538
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2}  .052194{col 58}{space 2} .0228234{col 69}{space 1}    2.29{col 78}{space 3}0.022{col 86}{space 4} .0074606{col 99}{space 3} .0969275
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2} .0618408{col 58}{space 2} .0304084{col 69}{space 1}    2.03{col 78}{space 3}0.042{col 86}{space 4} .0022408{col 99}{space 3} .1214409
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0783635{col 58}{space 2}  .026114{col 69}{space 1}   -3.00{col 78}{space 3}0.003{col 86}{space 4}-.1295466{col 99}{space 3}-.0271804
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2}-.0119007{col 58}{space 2}  .042549{col 69}{space 1}   -0.28{col 78}{space 3}0.780{col 86}{space 4}-.0952961{col 99}{space 3} .0714947
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1832325{col 58}{space 2} .0300567{col 69}{space 1}    6.10{col 78}{space 3}0.000{col 86}{space 4} .1243218{col 99}{space 3} .2421431
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}113153{txt}) = {res}6.882{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.2228
{col 25}{txt}Prob>|t| = {res}    0.8408

95%{txt} confidence set for null hypothesis expression: {res}[−.01137, .01559]

{txt}{col 1}Mean estimation{col 42}{lalign 13:Number of obs}{col 55} = {res}{ralign 7:113,372}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2761352{col 28}{space 2} .0013278{col 39}{space 5} .2735327{col 53}{space 3} .2787377
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_22_23_old.tex"'})

{com}. 
. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLES 24, 25, 26: Effects of Last Year's Lottery Prizes on Survey 
. *** Measures of Incumbent Party Support for Respondents by Education Attainment
. **----------------------------------------------------------------------------**
. 
. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. ** Period control:
. gen preperiod=1 if year_original<2010
{txt}(448,934 missing values generated)

{com}. replace preperiod=0 if year_original>=2010
{txt}(448,934 real changes made)

{com}. 
. global individual_characteristics "i.municipality_size female age i.status"
{txt}
{com}. 
. global ind_char_preperiod "preperiod#i.municipality_size preperiod#female preperiod#c.age preperiod#i.status"
{txt}
{com}. 
. 
. * Loop over education categories: education==1 (Primary education or below), education==2 (Secondary education), and education==3 (Higher education)
. 
. foreach e in 1 2 3 {c -(}
{txt}  2{com}.     
.     * Define label depending on education value
.     if `e' == 1 local edulabel "primary_or_below"
{txt}  3{com}.     else if `e' == 2 local edulabel "secondary"
{txt}  4{com}.     else if `e' == 3 local edulabel "higher"
{txt}  5{com}.     
.     preserve
{txt}  6{com}.         keep if education == `e' & month<4
{txt}  7{com}.         
.                 ** Col 4, Appendix Tables 24, 25, 26: 
.                 eststo b_preQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num $individual_characteristics if year_original<2010, absorb(survey) 
{txt}  8{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt}  9{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 10{com}.                 estadd local YearFE "$\checkmark$"
{txt} 11{com}.                 estadd local PeriodFE "$\times$"
{txt} 12{com}.                 estadd local PeriodCtrols "$\times$"
{txt} 13{com}.                 estadd local Data "Ours"
{txt} 14{com}.                 estadd local Sample "Pre-10"
{txt} 15{com}.                 estadd local Propensity "$\checkmark$"
{txt} 16{com}.                 estadd local Estimation "OLS"
{txt} 17{com}.                 estadd local Outcome "Q1"
{txt} 18{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 19{com}.                 mean(vote_incumbent) if e(sample)==1
{txt} 20{com}. 
.                 ** Col 5, Appendix Tables 24, 25, 26: 
.                 eststo b_postQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num $individual_characteristics if year_original>=2010, absorb(survey) 
{txt} 21{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 22{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 23{com}.                 estadd local YearFE "$\checkmark$"
{txt} 24{com}.                 estadd local PeriodFE "$\times$"
{txt} 25{com}.                 estadd local PeriodCtrols "$\times$"
{txt} 26{com}.                 estadd local Data "Ours"
{txt} 27{com}.                 estadd local Sample "Post-09"
{txt} 28{com}.                 estadd local Propensity "$\checkmark$"
{txt} 29{com}.                 estadd local Estimation "OLS"
{txt} 30{com}.                 estadd local Outcome "Q1"
{txt} 31{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 32{com}.                 mean(vote_incumbent) if e(sample)==1
{txt} 33{com}. 
.                 ** Col 6, Appendix Tables 24, 25, 26: 
.                 eststo b_pooledQ1: areg vote_incumbent top_prizes_gdp_ours c.expenditure_gdp_ours##i.year_original i.prov_num preperiod $individual_characteristics $ind_char_preperiod i.prov_num#preperiod, absorb(survey) 
{txt} 34{com}. 
.                 estadd local SurveyFE "$\checkmark$"
{txt} 35{com}.                 estadd local ProvinceFE "$\checkmark$"
{txt} 36{com}.                 estadd local YearFE "$\checkmark$"
{txt} 37{com}.                 estadd local PeriodFE "$\checkmark$"
{txt} 38{com}.                 estadd local PeriodCtrols "$\checkmark$"
{txt} 39{com}.                 estadd local Data "Ours"
{txt} 40{com}.                 estadd local Sample "Pooled"
{txt} 41{com}.                 estadd local Propensity "$\checkmark$"
{txt} 42{com}.                 estadd local Estimation "OLS"
{txt} 43{com}.                 estadd local Outcome "Q1"
{txt} 44{com}. 
.                 boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 45{com}.                 mean(vote_incumbent) if e(sample)==1
{txt} 46{com}. 
.                 
.                 *** Appendix Tables 24, 25, 26: Assemble tables 
.                 
.                 esttab b_preQ1 b_postQ1 b_pooledQ1 using ${c -(}tables{c )-}Survey_Appendix_Tables_24_25_26_`edulabel'.tex, ///
>                         keep(top_prizes_gdp_ours) nocon r2 nostar ///           
>                         cells(b(fmt(3)) se(fmt(3) par) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>                         mtitles se mtitles("Vote Incumb." "Vote Incumb." ///
>                         "Vote Incumb.")  ///
>                         coeflabels(top_prizes_gdp_ours "Top Lottery prizes" expenditure_gdp_ours "Lottery expenditure") ///
>                         scalars("Estimation Estimation" "Data Data" "Sample Sample" "Outcome Time Outcome" "ProvinceFE Province FE" "YearFE Year FE" "SurveyFE Survey FE" "Propensity Propensity corrected")  replace                           
{txt} 47{com}.                         
.                                 
.                 /* NOTE: The WCB confidence sets are manually added to the tables from the Stata output generated here. */
. 
.     restore
{txt} 48{com}. {c )-}
{txt}(943,026 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:85,970}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:85}, {res:85850})}{col 70} = {res}{ralign 6:28.24}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0327}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0314}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4486}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0003827{col 56}{space 2} .0053359{col 67}{space 1}    0.07{col 76}{space 3}0.943{col 84}{space 4}-.0100756{col 97}{space 3} .0108411
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0997245{col 56}{space 2} .1487737{col 67}{space 1}   -0.67{col 76}{space 3}0.503{col 84}{space 4}-.3913197{col 97}{space 3} .1918707
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}-.0140806{col 56}{space 2} .2052435{col 67}{space 1}   -0.07{col 76}{space 3}0.945{col 84}{space 4}-.4163562{col 97}{space 3}  .388195
{txt}{space 37}1989  {c |}{col 44}{res}{space 2}-.0174914{col 56}{space 2} .1685981{col 67}{space 1}   -0.10{col 76}{space 3}0.917{col 84}{space 4}-.3479423{col 97}{space 3} .3129595
{txt}{space 37}1990  {c |}{col 44}{res}{space 2}-.0987216{col 56}{space 2} .1692978{col 67}{space 1}   -0.58{col 76}{space 3}0.560{col 84}{space 4}-.4305437{col 97}{space 3} .2331006
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.1443464{col 56}{space 2} .2089363{col 67}{space 1}   -0.69{col 76}{space 3}0.490{col 84}{space 4}-.5538599{col 97}{space 3}  .265167
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.0406754{col 56}{space 2} .1771415{col 67}{space 1}   -0.23{col 76}{space 3}0.818{col 84}{space 4}-.3878713{col 97}{space 3} .3065205
{txt}{space 37}1993  {c |}{col 44}{res}{space 2} .1090377{col 56}{space 2} .1655869{col 67}{space 1}    0.66{col 76}{space 3}0.510{col 84}{space 4}-.2155113{col 97}{space 3} .4335867
{txt}{space 37}1994  {c |}{col 44}{res}{space 2} .1231463{col 56}{space 2} .1689247{col 67}{space 1}    0.73{col 76}{space 3}0.466{col 84}{space 4}-.2079447{col 97}{space 3} .4542373
{txt}{space 37}1995  {c |}{col 44}{res}{space 2} .0576861{col 56}{space 2} .1646756{col 67}{space 1}    0.35{col 76}{space 3}0.726{col 84}{space 4}-.2650768{col 97}{space 3}  .380449
{txt}{space 37}1996  {c |}{col 44}{res}{space 2} .1009302{col 56}{space 2} .1816946{col 67}{space 1}    0.56{col 76}{space 3}0.579{col 84}{space 4}-.2551898{col 97}{space 3} .4570501
{txt}{space 37}1997  {c |}{col 44}{res}{space 2} .6265786{col 56}{space 2} .2051635{col 67}{space 1}    3.05{col 76}{space 3}0.002{col 84}{space 4} .2244599{col 97}{space 3} 1.028697
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .3432433{col 56}{space 2} .2175841{col 67}{space 1}    1.58{col 76}{space 3}0.115{col 84}{space 4}-.0832198{col 97}{space 3} .7697063
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .5798999{col 56}{space 2} .2147843{col 67}{space 1}    2.70{col 76}{space 3}0.007{col 84}{space 4} .1589245{col 97}{space 3} 1.000875
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .6526139{col 56}{space 2} .1502479{col 67}{space 1}    4.34{col 76}{space 3}0.000{col 84}{space 4} .3581292{col 97}{space 3} .9470985
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} .5875847{col 56}{space 2} .2082149{col 67}{space 1}    2.82{col 76}{space 3}0.005{col 84}{space 4} .1794853{col 97}{space 3}  .995684
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} .5225092{col 56}{space 2} .2116055{col 67}{space 1}    2.47{col 76}{space 3}0.014{col 84}{space 4} .1077643{col 97}{space 3} .9372541
{txt}{space 37}2003  {c |}{col 44}{res}{space 2} .4527143{col 56}{space 2} .2013172{col 67}{space 1}    2.25{col 76}{space 3}0.025{col 84}{space 4} .0581343{col 97}{space 3} .8472943
{txt}{space 37}2004  {c |}{col 44}{res}{space 2} .5990778{col 56}{space 2} .1494528{col 67}{space 1}    4.01{col 76}{space 3}0.000{col 84}{space 4} .3061515{col 97}{space 3} .8920041
{txt}{space 37}2005  {c |}{col 44}{res}{space 2} -.091818{col 56}{space 2} .2005384{col 67}{space 1}   -0.46{col 76}{space 3}0.647{col 84}{space 4}-.4848716{col 97}{space 3} .3012356
{txt}{space 37}2006  {c |}{col 44}{res}{space 2}-.1322776{col 56}{space 2} .1952227{col 67}{space 1}   -0.68{col 76}{space 3}0.498{col 84}{space 4}-.5149125{col 97}{space 3} .2503572
{txt}{space 37}2007  {c |}{col 44}{res}{space 2} .0926231{col 56}{space 2} .1822912{col 67}{space 1}    0.51{col 76}{space 3}0.611{col 84}{space 4}-.2646661{col 97}{space 3} .4499123
{txt}{space 37}2008  {c |}{col 44}{res}{space 2} .0681783{col 56}{space 2} .1494869{col 67}{space 1}    0.46{col 76}{space 3}0.648{col 84}{space 4}-.2248149{col 97}{space 3} .3611714
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}-.2042156{col 56}{space 2}  .194034{col 67}{space 1}   -1.05{col 76}{space 3}0.293{col 84}{space 4}-.5845207{col 97}{space 3} .1760894
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.0875982{col 56}{space 2} .0191866{col 67}{space 1}   -4.57{col 76}{space 3}0.000{col 84}{space 4}-.1252038{col 97}{space 3}-.0499927
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0804749{col 56}{space 2} .0162006{col 67}{space 1}    4.97{col 76}{space 3}0.000{col 84}{space 4} .0487217{col 97}{space 3}  .112228
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0974443{col 56}{space 2} .0130838{col 67}{space 1}    7.45{col 76}{space 3}0.000{col 84}{space 4} .0718002{col 97}{space 3} .1230885
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0713293{col 56}{space 2} .0156538{col 67}{space 1}    4.56{col 76}{space 3}0.000{col 84}{space 4} .0406479{col 97}{space 3} .1020107
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2}-.0052776{col 56}{space 2}  .013443{col 67}{space 1}   -0.39{col 76}{space 3}0.695{col 84}{space 4}-.0316259{col 97}{space 3} .0210706
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}    .0631{col 56}{space 2} .0193348{col 67}{space 1}    3.26{col 76}{space 3}0.001{col 84}{space 4} .0252039{col 97}{space 3} .1009961
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .1121117{col 56}{space 2} .0151324{col 67}{space 1}    7.41{col 76}{space 3}0.000{col 84}{space 4} .0824523{col 97}{space 3}  .141771
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2} .0081723{col 56}{space 2} .0113266{col 67}{space 1}    0.72{col 76}{space 3}0.471{col 84}{space 4}-.0140278{col 97}{space 3} .0303724
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0087947{col 56}{space 2} .0172186{col 67}{space 1}    0.51{col 76}{space 3}0.610{col 84}{space 4}-.0249535{col 97}{space 3}  .042543
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0986676{col 56}{space 2} .0156262{col 67}{space 1}    6.31{col 76}{space 3}0.000{col 84}{space 4} .0680404{col 97}{space 3} .1292948
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0891782{col 56}{space 2} .0154165{col 67}{space 1}    5.78{col 76}{space 3}0.000{col 84}{space 4} .0589619{col 97}{space 3} .1193944
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2}  .088996{col 56}{space 2} .0159602{col 67}{space 1}    5.58{col 76}{space 3}0.000{col 84}{space 4}  .057714{col 97}{space 3} .1202779
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0830334{col 56}{space 2} .0153033{col 67}{space 1}    5.43{col 76}{space 3}0.000{col 84}{space 4} .0530392{col 97}{space 3} .1130277
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0562576{col 56}{space 2} .0158416{col 67}{space 1}    3.55{col 76}{space 3}0.000{col 84}{space 4} .0252081{col 97}{space 3}  .087307
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2} -.031216{col 56}{space 2} .0142756{col 67}{space 1}   -2.19{col 76}{space 3}0.029{col 84}{space 4}-.0591961{col 97}{space 3}-.0032359
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0543737{col 56}{space 2} .0183653{col 67}{space 1}    2.96{col 76}{space 3}0.003{col 84}{space 4} .0183779{col 97}{space 3} .0903695
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1624439{col 56}{space 2}  .015342{col 67}{space 1}  -10.59{col 76}{space 3}0.000{col 84}{space 4} -.192514{col 97}{space 3}-.1323737
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0616507{col 56}{space 2}   .01676{col 67}{space 1}   -3.68{col 76}{space 3}0.000{col 84}{space 4}-.0945002{col 97}{space 3}-.0288012
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0341767{col 56}{space 2}  .014491{col 67}{space 1}    2.36{col 76}{space 3}0.018{col 84}{space 4} .0057745{col 97}{space 3} .0625789
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0610794{col 56}{space 2} .0196526{col 67}{space 1}    3.11{col 76}{space 3}0.002{col 84}{space 4} .0225605{col 97}{space 3} .0995984
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0779211{col 56}{space 2}  .017767{col 67}{space 1}    4.39{col 76}{space 3}0.000{col 84}{space 4} .0430979{col 97}{space 3} .1127442
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0191899{col 56}{space 2} .0185411{col 67}{space 1}   -1.03{col 76}{space 3}0.301{col 84}{space 4}-.0555303{col 97}{space 3} .0171505
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0571178{col 56}{space 2} .0161936{col 67}{space 1}    3.53{col 76}{space 3}0.000{col 84}{space 4} .0253785{col 97}{space 3} .0888571
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0442548{col 56}{space 2}  .014502{col 67}{space 1}    3.05{col 76}{space 3}0.002{col 84}{space 4} .0158311{col 97}{space 3} .0726785
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2}-.0175129{col 56}{space 2} .0185841{col 67}{space 1}   -0.94{col 76}{space 3}0.346{col 84}{space 4}-.0539375{col 97}{space 3} .0189117
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0961996{col 56}{space 2} .0156501{col 67}{space 1}    6.15{col 76}{space 3}0.000{col 84}{space 4} .0655256{col 97}{space 3} .1268736
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0178619{col 56}{space 2}  .015687{col 67}{space 1}    1.14{col 76}{space 3}0.255{col 84}{space 4}-.0128845{col 97}{space 3} .0486084
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0893316{col 56}{space 2} .0182525{col 67}{space 1}   -4.89{col 76}{space 3}0.000{col 84}{space 4}-.1251064{col 97}{space 3}-.0535569
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0687269{col 56}{space 2}   .01628{col 67}{space 1}    4.22{col 76}{space 3}0.000{col 84}{space 4} .0368182{col 97}{space 3} .1006356
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0188693{col 56}{space 2} .0121865{col 67}{space 1}    1.55{col 76}{space 3}0.122{col 84}{space 4}-.0050161{col 97}{space 3} .0427547
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .1064632{col 56}{space 2} .0138464{col 67}{space 1}    7.69{col 76}{space 3}0.000{col 84}{space 4} .0793244{col 97}{space 3} .1336021
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0489984{col 56}{space 2} .0135139{col 67}{space 1}    3.63{col 76}{space 3}0.000{col 84}{space 4} .0225111{col 97}{space 3} .0754856
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0259421{col 56}{space 2} .0164498{col 67}{space 1}   -1.58{col 76}{space 3}0.115{col 84}{space 4}-.0581836{col 97}{space 3} .0062994
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0610762{col 56}{space 2} .0162478{col 67}{space 1}    3.76{col 76}{space 3}0.000{col 84}{space 4} .0292307{col 97}{space 3} .0929217
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0111735{col 56}{space 2} .0200132{col 67}{space 1}    0.56{col 76}{space 3}0.577{col 84}{space 4}-.0280522{col 97}{space 3} .0503992
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} -.019658{col 56}{space 2} .0138373{col 67}{space 1}   -1.42{col 76}{space 3}0.155{col 84}{space 4}-.0467791{col 97}{space 3}  .007463
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .1247474{col 56}{space 2} .0164055{col 67}{space 1}    7.60{col 76}{space 3}0.000{col 84}{space 4} .0925926{col 97}{space 3} .1569021
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0276877{col 56}{space 2} .0149663{col 67}{space 1}    1.85{col 76}{space 3}0.064{col 84}{space 4}-.0016462{col 97}{space 3} .0570216
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2}  .020832{col 56}{space 2} .0208995{col 67}{space 1}    1.00{col 76}{space 3}0.319{col 84}{space 4}-.0201308{col 97}{space 3} .0617949
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0948695{col 56}{space 2} .0136197{col 67}{space 1}    6.97{col 76}{space 3}0.000{col 84}{space 4}  .068175{col 97}{space 3} .1215641
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0502815{col 56}{space 2} .0254115{col 67}{space 1}    1.98{col 76}{space 3}0.048{col 84}{space 4} .0004752{col 97}{space 3} .1000879
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2} .0076791{col 56}{space 2} .0157759{col 67}{space 1}    0.49{col 76}{space 3}0.626{col 84}{space 4}-.0232415{col 97}{space 3} .0385997
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0385392{col 56}{space 2} .0177407{col 67}{space 1}    2.17{col 76}{space 3}0.030{col 84}{space 4} .0037676{col 97}{space 3} .0733109
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0437518{col 56}{space 2} .0159847{col 67}{space 1}    2.74{col 76}{space 3}0.006{col 84}{space 4}  .012422{col 97}{space 3} .0750817
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0617942{col 56}{space 2} .0127687{col 67}{space 1}    4.84{col 76}{space 3}0.000{col 84}{space 4} .0367678{col 97}{space 3} .0868207
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0550021{col 56}{space 2}  .016985{col 67}{space 1}    3.24{col 76}{space 3}0.001{col 84}{space 4} .0217116{col 97}{space 3} .0882927
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1810819{col 56}{space 2} .0138713{col 67}{space 1}  -13.05{col 76}{space 3}0.000{col 84}{space 4}-.2082695{col 97}{space 3}-.1538943
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0922394{col 56}{space 2}    .0187{col 67}{space 1}    4.93{col 76}{space 3}0.000{col 84}{space 4} .0555875{col 97}{space 3} .1288913
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0039876{col 56}{space 2} .0156181{col 67}{space 1}    0.26{col 76}{space 3}0.798{col 84}{space 4}-.0266237{col 97}{space 3} .0345989
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0026051{col 56}{space 2} .0058089{col 67}{space 1}   -0.45{col 76}{space 3}0.654{col 84}{space 4}-.0139904{col 97}{space 3} .0087803
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0053104{col 56}{space 2} .0058701{col 67}{space 1}   -0.90{col 76}{space 3}0.366{col 84}{space 4}-.0168158{col 97}{space 3} .0061949
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0279912{col 56}{space 2} .0071702{col 67}{space 1}   -3.90{col 76}{space 3}0.000{col 84}{space 4}-.0420446{col 97}{space 3}-.0139377
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0068643{col 56}{space 2}  .006156{col 67}{space 1}   -1.12{col 76}{space 3}0.265{col 84}{space 4}-.0189301{col 97}{space 3} .0052015
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0458552{col 56}{space 2} .0098024{col 67}{space 1}   -4.68{col 76}{space 3}0.000{col 84}{space 4}-.0650677{col 97}{space 3}-.0266426
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0316047{col 56}{space 2} .0095029{col 67}{space 1}   -3.33{col 76}{space 3}0.001{col 84}{space 4}-.0502303{col 97}{space 3}-.0129791
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0293184{col 56}{space 2} .0037317{col 67}{space 1}   -7.86{col 76}{space 3}0.000{col 84}{space 4}-.0366325{col 97}{space 3}-.0220043
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0008009{col 56}{space 2} .0001223{col 67}{space 1}    6.55{col 76}{space 3}0.000{col 84}{space 4} .0005613{col 97}{space 3} .0010406
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0038947{col 56}{space 2} .0058551{col 67}{space 1}   -0.67{col 76}{space 3}0.506{col 84}{space 4}-.0153707{col 97}{space 3} .0075813
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0316665{col 56}{space 2} .0052463{col 67}{space 1}    6.04{col 76}{space 3}0.000{col 84}{space 4} .0213838{col 97}{space 3} .0419492
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0377451{col 56}{space 2}  .012457{col 67}{space 1}   -3.03{col 76}{space 3}0.002{col 84}{space 4}-.0621608{col 97}{space 3}-.0133295
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0169425{col 56}{space 2} .0049881{col 67}{space 1}    3.40{col 76}{space 3}0.001{col 84}{space 4} .0071658{col 97}{space 3} .0267191
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1896907{col 56}{space 2} .0182199{col 67}{space 1}   10.41{col 76}{space 3}0.000{col 84}{space 4} .1539798{col 97}{space 3} .2254016
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}85850{txt}) = {res}12.482{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.0388
{col 25}{txt}Prob>|t| = {res}    0.9680

95%{txt} confidence set for null hypothesis expression: {res}[−.02203, .02917]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:85,970}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2944399{col 28}{space 2} .0015545{col 39}{space 5} .2913931{col 53}{space 3} .2974868
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:30,397}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:75}, {res:30301})}{col 70} = {res}{ralign 6:9.97}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0340}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0310}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4233}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0022271{col 56}{space 2} .0077263{col 67}{space 1}   -0.29{col 76}{space 3}0.773{col 84}{space 4}-.0173709{col 97}{space 3} .0129168
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.5443172{col 56}{space 2} .1896325{col 67}{space 1}   -2.87{col 76}{space 3}0.004{col 84}{space 4}-.9160049{col 97}{space 3}-.1726295
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2} .0978869{col 56}{space 2} .2119445{col 67}{space 1}    0.46{col 76}{space 3}0.644{col 84}{space 4}-.3175332{col 97}{space 3} .5133071
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .8449154{col 56}{space 2} .2079167{col 67}{space 1}    4.06{col 76}{space 3}0.000{col 84}{space 4} .4373899{col 97}{space 3} 1.252441
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .7179398{col 56}{space 2} .2000755{col 67}{space 1}    3.59{col 76}{space 3}0.000{col 84}{space 4} .3257834{col 97}{space 3} 1.110096
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .8588089{col 56}{space 2} .2225156{col 67}{space 1}    3.86{col 76}{space 3}0.000{col 84}{space 4} .4226688{col 97}{space 3} 1.294949
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} 1.012119{col 56}{space 2} .2286233{col 67}{space 1}    4.43{col 76}{space 3}0.000{col 84}{space 4} .5640081{col 97}{space 3} 1.460231
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .7637398{col 56}{space 2} .2019544{col 67}{space 1}    3.78{col 76}{space 3}0.000{col 84}{space 4} .3679007{col 97}{space 3} 1.159579
{txt}{space 37}2017  {c |}{col 44}{res}{space 2}  .920985{col 56}{space 2} .2103302{col 67}{space 1}    4.38{col 76}{space 3}0.000{col 84}{space 4} .5087289{col 97}{space 3} 1.333241
{txt}{space 37}2018  {c |}{col 44}{res}{space 2} .7463786{col 56}{space 2} .2173803{col 67}{space 1}    3.43{col 76}{space 3}0.001{col 84}{space 4} .3203039{col 97}{space 3} 1.172453
{txt}{space 37}2019  {c |}{col 44}{res}{space 2} .2481513{col 56}{space 2} .1556538{col 67}{space 1}    1.59{col 76}{space 3}0.111{col 84}{space 4}-.0569367{col 97}{space 3} .5532393
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .3631707{col 56}{space 2} .1661966{col 67}{space 1}    2.19{col 76}{space 3}0.029{col 84}{space 4} .0374183{col 97}{space 3}  .688923
{txt}{space 37}2021  {c |}{col 44}{res}{space 2} .2434374{col 56}{space 2} .1827269{col 67}{space 1}    1.33{col 76}{space 3}0.183{col 84}{space 4} -.114715{col 97}{space 3} .6015898
{txt}{space 37}2022  {c |}{col 44}{res}{space 2} .3114062{col 56}{space 2} .1720285{col 67}{space 1}    1.81{col 76}{space 3}0.070{col 84}{space 4}-.0257769{col 97}{space 3} .6485893
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1285583{col 56}{space 2} .0393463{col 67}{space 1}   -3.27{col 76}{space 3}0.001{col 84}{space 4}-.2056787{col 97}{space 3}-.0514379
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0214031{col 56}{space 2} .0289044{col 67}{space 1}    0.74{col 76}{space 3}0.459{col 84}{space 4}-.0352507{col 97}{space 3} .0780569
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0762067{col 56}{space 2} .0209706{col 67}{space 1}    3.63{col 76}{space 3}0.000{col 84}{space 4} .0351034{col 97}{space 3}   .11731
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0570139{col 56}{space 2} .0230358{col 67}{space 1}    2.48{col 76}{space 3}0.013{col 84}{space 4} .0118628{col 97}{space 3} .1021651
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2}-.0094035{col 56}{space 2} .0263328{col 67}{space 1}   -0.36{col 76}{space 3}0.721{col 84}{space 4}-.0610169{col 97}{space 3}   .04221
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}-.0117057{col 56}{space 2} .0394661{col 67}{space 1}   -0.30{col 76}{space 3}0.767{col 84}{space 4} -.089061{col 97}{space 3} .0656496
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0831255{col 56}{space 2}  .024285{col 67}{space 1}    3.42{col 76}{space 3}0.001{col 84}{space 4} .0355258{col 97}{space 3} .1307252
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0340444{col 56}{space 2} .0240639{col 67}{space 1}   -1.41{col 76}{space 3}0.157{col 84}{space 4}-.0812106{col 97}{space 3} .0131218
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0314613{col 56}{space 2} .0368259{col 67}{space 1}    0.85{col 76}{space 3}0.393{col 84}{space 4}-.0407189{col 97}{space 3} .1036416
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .1110429{col 56}{space 2} .0287008{col 67}{space 1}    3.87{col 76}{space 3}0.000{col 84}{space 4} .0547882{col 97}{space 3} .1672976
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0085089{col 56}{space 2} .0235005{col 67}{space 1}    0.36{col 76}{space 3}0.717{col 84}{space 4}-.0375531{col 97}{space 3} .0545709
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0012254{col 56}{space 2} .0247229{col 67}{space 1}    0.05{col 76}{space 3}0.960{col 84}{space 4}-.0472325{col 97}{space 3} .0496833
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0404557{col 56}{space 2} .0250048{col 67}{space 1}    1.62{col 76}{space 3}0.106{col 84}{space 4}-.0085548{col 97}{space 3} .0894662
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0455687{col 56}{space 2} .0284742{col 67}{space 1}    1.60{col 76}{space 3}0.110{col 84}{space 4}-.0102419{col 97}{space 3} .1013792
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2} .0224846{col 56}{space 2} .0226137{col 67}{space 1}    0.99{col 76}{space 3}0.320{col 84}{space 4}-.0218392{col 97}{space 3} .0668084
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0417385{col 56}{space 2} .0363401{col 67}{space 1}    1.15{col 76}{space 3}0.251{col 84}{space 4}-.0294896{col 97}{space 3} .1129665
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1117187{col 56}{space 2} .0307194{col 67}{space 1}   -3.64{col 76}{space 3}0.000{col 84}{space 4}  -.17193{col 97}{space 3}-.0515075
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0916262{col 56}{space 2} .0316197{col 67}{space 1}   -2.90{col 76}{space 3}0.004{col 84}{space 4}-.1536022{col 97}{space 3}-.0296502
{txt}{space 34}granada  {c |}{col 44}{res}{space 2}  .090312{col 56}{space 2} .0238129{col 67}{space 1}    3.79{col 76}{space 3}0.000{col 84}{space 4} .0436378{col 97}{space 3} .1369863
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .0875056{col 56}{space 2} .0318147{col 67}{space 1}    2.75{col 76}{space 3}0.006{col 84}{space 4} .0251474{col 97}{space 3} .1498639
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0431184{col 56}{space 2} .0312915{col 67}{space 1}    1.38{col 76}{space 3}0.168{col 84}{space 4}-.0182142{col 97}{space 3}  .104451
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0173465{col 56}{space 2} .0371213{col 67}{space 1}    0.47{col 76}{space 3}0.640{col 84}{space 4}-.0554129{col 97}{space 3} .0901059
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}-.0215322{col 56}{space 2}  .030518{col 67}{space 1}   -0.71{col 76}{space 3}0.480{col 84}{space 4}-.0813487{col 97}{space 3} .0382843
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0479913{col 56}{space 2} .0240611{col 67}{space 1}    1.99{col 76}{space 3}0.046{col 84}{space 4} .0008306{col 97}{space 3}  .095152
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0666957{col 56}{space 2} .0280242{col 67}{space 1}    2.38{col 76}{space 3}0.017{col 84}{space 4}  .011767{col 97}{space 3} .1216243
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0158703{col 56}{space 2} .0283687{col 67}{space 1}    0.56{col 76}{space 3}0.576{col 84}{space 4}-.0397335{col 97}{space 3} .0714742
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0297789{col 56}{space 2} .0298145{col 67}{space 1}    1.00{col 76}{space 3}0.318{col 84}{space 4}-.0286587{col 97}{space 3} .0882165
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0850773{col 56}{space 2} .0317523{col 67}{space 1}   -2.68{col 76}{space 3}0.007{col 84}{space 4}-.1473132{col 97}{space 3}-.0228415
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0355768{col 56}{space 2} .0276756{col 67}{space 1}    1.29{col 76}{space 3}0.199{col 84}{space 4}-.0186685{col 97}{space 3} .0898221
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0084531{col 56}{space 2} .0213045{col 67}{space 1}    0.40{col 76}{space 3}0.692{col 84}{space 4}-.0333047{col 97}{space 3}  .050211
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2}  .064644{col 56}{space 2} .0209668{col 67}{space 1}    3.08{col 76}{space 3}0.002{col 84}{space 4} .0235481{col 97}{space 3} .1057399
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0360666{col 56}{space 2} .0208522{col 67}{space 1}    1.73{col 76}{space 3}0.084{col 84}{space 4}-.0048045{col 97}{space 3} .0769378
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}  .007539{col 56}{space 2} .0299439{col 67}{space 1}    0.25{col 76}{space 3}0.801{col 84}{space 4}-.0511523{col 97}{space 3} .0662303
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0198344{col 56}{space 2}  .029759{col 67}{space 1}    0.67{col 76}{space 3}0.505{col 84}{space 4}-.0384946{col 97}{space 3} .0781633
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0153152{col 56}{space 2} .0374351{col 67}{space 1}    0.41{col 76}{space 3}0.682{col 84}{space 4}-.0580592{col 97}{space 3} .0886896
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0149314{col 56}{space 2} .0228177{col 67}{space 1}   -0.65{col 76}{space 3}0.513{col 84}{space 4} -.059655{col 97}{space 3} .0297922
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0366332{col 56}{space 2} .0290491{col 67}{space 1}    1.26{col 76}{space 3}0.207{col 84}{space 4}-.0203043{col 97}{space 3} .0935708
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2}  .054515{col 56}{space 2} .0236612{col 67}{space 1}    2.30{col 76}{space 3}0.021{col 84}{space 4}  .008138{col 97}{space 3}  .100892
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0818915{col 56}{space 2} .0553071{col 67}{space 1}    1.48{col 76}{space 3}0.139{col 84}{space 4}-.0265127{col 97}{space 3} .1902958
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0254211{col 56}{space 2} .0235114{col 67}{space 1}    1.08{col 76}{space 3}0.280{col 84}{space 4}-.0206623{col 97}{space 3} .0715046
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0454277{col 56}{space 2} .0893027{col 67}{space 1}    0.51{col 76}{space 3}0.611{col 84}{space 4}-.1296094{col 97}{space 3} .2204647
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0518295{col 56}{space 2} .0310356{col 67}{space 1}   -1.67{col 76}{space 3}0.095{col 84}{space 4}-.1126605{col 97}{space 3} .0090015
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} -.036376{col 56}{space 2} .0383349{col 67}{space 1}   -0.95{col 76}{space 3}0.343{col 84}{space 4}-.1115139{col 97}{space 3} .0387619
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0459331{col 56}{space 2} .0238471{col 67}{space 1}    1.93{col 76}{space 3}0.054{col 84}{space 4}-.0008082{col 97}{space 3} .0926745
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0401547{col 56}{space 2} .0194163{col 67}{space 1}    2.07{col 76}{space 3}0.039{col 84}{space 4} .0020979{col 97}{space 3} .0782115
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}-.0154753{col 56}{space 2} .0264229{col 67}{space 1}   -0.59{col 76}{space 3}0.558{col 84}{space 4}-.0672653{col 97}{space 3} .0363148
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.0899308{col 56}{space 2} .0232273{col 67}{space 1}   -3.87{col 76}{space 3}0.000{col 84}{space 4}-.1354573{col 97}{space 3}-.0444042
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0974753{col 56}{space 2}  .039774{col 67}{space 1}    2.45{col 76}{space 3}0.014{col 84}{space 4} .0195166{col 97}{space 3}  .175434
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2}  .023811{col 56}{space 2}  .024335{col 67}{space 1}    0.98{col 76}{space 3}0.328{col 84}{space 4}-.0238867{col 97}{space 3} .0715088
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0009656{col 56}{space 2} .0098174{col 67}{space 1}   -0.10{col 76}{space 3}0.922{col 84}{space 4}-.0202081{col 97}{space 3} .0182768
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0026399{col 56}{space 2}  .009601{col 67}{space 1}   -0.27{col 76}{space 3}0.783{col 84}{space 4}-.0214583{col 97}{space 3} .0161785
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0131743{col 56}{space 2} .0112164{col 67}{space 1}   -1.17{col 76}{space 3}0.240{col 84}{space 4} -.035159{col 97}{space 3} .0088104
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0028459{col 56}{space 2} .0101823{col 67}{space 1}    0.28{col 76}{space 3}0.780{col 84}{space 4}-.0171118{col 97}{space 3} .0228036
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0063445{col 56}{space 2} .0146187{col 67}{space 1}   -0.43{col 76}{space 3}0.664{col 84}{space 4}-.0349977{col 97}{space 3} .0223088
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0074545{col 56}{space 2} .0156513{col 67}{space 1}    0.48{col 76}{space 3}0.634{col 84}{space 4}-.0232226{col 97}{space 3} .0381317
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0098132{col 56}{space 2} .0052116{col 67}{space 1}   -1.88{col 76}{space 3}0.060{col 84}{space 4}-.0200281{col 97}{space 3} .0004018
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0018818{col 56}{space 2} .0002195{col 67}{space 1}    8.57{col 76}{space 3}0.000{col 84}{space 4} .0014517{col 97}{space 3}  .002312
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0171287{col 56}{space 2}  .007514{col 67}{space 1}    2.28{col 76}{space 3}0.023{col 84}{space 4}  .002401{col 97}{space 3} .0318564
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0454313{col 56}{space 2} .0084165{col 67}{space 1}    5.40{col 76}{space 3}0.000{col 84}{space 4} .0289346{col 97}{space 3}  .061928
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0053361{col 56}{space 2} .0195488{col 67}{space 1}    0.27{col 76}{space 3}0.785{col 84}{space 4}-.0329804{col 97}{space 3} .0436525
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0356694{col 56}{space 2} .0100405{col 67}{space 1}    3.55{col 76}{space 3}0.000{col 84}{space 4} .0159895{col 97}{space 3} .0553492
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1391472{col 56}{space 2} .0455817{col 67}{space 1}    3.05{col 76}{space 3}0.002{col 84}{space 4}  .049805{col 97}{space 3} .2284894
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}30301{txt}) = {res}8.167{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.2784
{col 25}{txt}Prob>|t| = {res}    0.7768

95%{txt} confidence set for null hypothesis expression: {res}[−.02578, .01729]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:30,397}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2448268{col 28}{space 2} .0024663{col 39}{space 5} .2399928{col 53}{space 3} .2496608
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#0b.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#1.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 52}{lalign 17:Number of obs}{col 69} = {res}{ralign 7:116,367}
{txt}{col 1}Absorbed variable: {res:survey}{col 52}{lalign 17:No. of categories}{col 69} = {res}{ralign 7:56}
{txt}{col 52}{lalign 17:F({res:159}, {res:116152})}{col 69} = {res}{ralign 7:19.85}
{txt}{col 52}{lalign 17:Prob > F}{col 69} = {res}{ralign 7:0.0000}
{txt}{col 52}{lalign 17:R-squared}{col 69} = {res}{ralign 7:0.0353}
{txt}{col 52}{lalign 17:Adj R-squared}{col 69} = {res}{ralign 7:0.0335}
{txt}{col 52}{lalign 17:Root MSE}{col 69} = {res}{ralign 7:0.4421}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2}-.0003952{col 58}{space 2} .0044061{col 69}{space 1}   -0.09{col 78}{space 3}0.929{col 86}{space 4} -.009031{col 99}{space 3} .0082406
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2}-.1001019{col 58}{space 2} .1466229{col 69}{space 1}   -0.68{col 78}{space 3}0.495{col 86}{space 4}-.3874805{col 99}{space 3} .1872767
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}-.0136166{col 58}{space 2} .2022782{col 69}{space 1}   -0.07{col 78}{space 3}0.946{col 86}{space 4}-.4100788{col 99}{space 3} .3828456
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.0173083{col 58}{space 2} .1661669{col 69}{space 1}   -0.10{col 78}{space 3}0.917{col 86}{space 4}-.3429928{col 99}{space 3} .3083762
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.0984545{col 58}{space 2} .1668549{col 69}{space 1}   -0.59{col 78}{space 3}0.555{col 86}{space 4}-.4254875{col 99}{space 3} .2285784
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}-.1439759{col 58}{space 2} .2059205{col 69}{space 1}   -0.70{col 78}{space 3}0.484{col 86}{space 4}-.5475769{col 99}{space 3} .2596252
{txt}{space 39}1992  {c |}{col 46}{res}{space 2} -.040287{col 58}{space 2} .1745826{col 69}{space 1}   -0.23{col 78}{space 3}0.818{col 86}{space 4}-.3824663{col 99}{space 3} .3018922
{txt}{space 39}1993  {c |}{col 46}{res}{space 2} .1087824{col 58}{space 2} .1631977{col 69}{space 1}    0.67{col 78}{space 3}0.505{col 86}{space 4}-.2110826{col 99}{space 3} .4286474
{txt}{space 39}1994  {c |}{col 46}{res}{space 2} .1231528{col 58}{space 2} .1664901{col 69}{space 1}    0.74{col 78}{space 3}0.459{col 86}{space 4}-.2031652{col 99}{space 3} .4494708
{txt}{space 39}1995  {c |}{col 46}{res}{space 2} .0578281{col 58}{space 2} .1623014{col 69}{space 1}    0.36{col 78}{space 3}0.722{col 86}{space 4}-.2602802{col 99}{space 3} .3759364
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}  .099995{col 58}{space 2} .1790427{col 69}{space 1}    0.56{col 78}{space 3}0.577{col 86}{space 4}-.2509259{col 99}{space 3} .4509159
{txt}{space 39}1997  {c |}{col 46}{res}{space 2} .6275153{col 58}{space 2}  .202177{col 69}{space 1}    3.10{col 78}{space 3}0.002{col 86}{space 4} .2312514{col 99}{space 3} 1.023779
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .3432621{col 58}{space 2} .2144482{col 69}{space 1}    1.60{col 78}{space 3}0.109{col 86}{space 4}-.0770531{col 99}{space 3} .7635773
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .5791663{col 58}{space 2} .2116714{col 69}{space 1}    2.74{col 78}{space 3}0.006{col 86}{space 4} .1642936{col 99}{space 3}  .994039
{txt}{space 39}2000  {c |}{col 46}{res}{space 2} .6527146{col 58}{space 2}  .148082{col 69}{space 1}    4.41{col 78}{space 3}0.000{col 86}{space 4} .3624761{col 99}{space 3} .9429531
{txt}{space 39}2001  {c |}{col 46}{res}{space 2} .5883989{col 58}{space 2}  .205192{col 69}{space 1}    2.87{col 78}{space 3}0.004{col 86}{space 4} .1862258{col 99}{space 3}  .990572
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .5221959{col 58}{space 2} .2085525{col 69}{space 1}    2.50{col 78}{space 3}0.012{col 86}{space 4} .1134361{col 99}{space 3} .9309556
{txt}{space 39}2003  {c |}{col 46}{res}{space 2} .4529544{col 58}{space 2} .1984138{col 69}{space 1}    2.28{col 78}{space 3}0.022{col 86}{space 4} .0640665{col 99}{space 3} .8418423
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} .5994588{col 58}{space 2} .1472922{col 69}{space 1}    4.07{col 78}{space 3}0.000{col 86}{space 4} .3107685{col 99}{space 3} .8881492
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}-.0905187{col 58}{space 2}   .19759{col 69}{space 1}   -0.46{col 78}{space 3}0.647{col 86}{space 4}-.4777921{col 99}{space 3} .2967546
{txt}{space 39}2006  {c |}{col 46}{res}{space 2}-.1327007{col 58}{space 2} .1924028{col 69}{space 1}   -0.69{col 78}{space 3}0.490{col 86}{space 4}-.5098071{col 99}{space 3} .2444057
{txt}{space 39}2007  {c |}{col 46}{res}{space 2} .0924639{col 58}{space 2}  .179663{col 69}{space 1}    0.51{col 78}{space 3}0.607{col 86}{space 4}-.2596728{col 99}{space 3} .4446006
{txt}{space 39}2008  {c |}{col 46}{res}{space 2} .0681666{col 58}{space 2} .1473325{col 69}{space 1}    0.46{col 78}{space 3}0.644{col 86}{space 4}-.2206028{col 99}{space 3} .3569359
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.2046999{col 58}{space 2} .1912292{col 69}{space 1}   -1.07{col 78}{space 3}0.284{col 86}{space 4}-.5795062{col 99}{space 3} .1701063
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}-.4419461{col 58}{space 2} .2462495{col 69}{space 1}   -1.79{col 78}{space 3}0.073{col 86}{space 4}-.9245913{col 99}{space 3} .0406991
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.3447171{col 58}{space 2} .2612802{col 69}{space 1}   -1.32{col 78}{space 3}0.187{col 86}{space 4}-.8568222{col 99}{space 3}  .167388
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .4016467{col 58}{space 2} .2547433{col 69}{space 1}    1.58{col 78}{space 3}0.115{col 86}{space 4}-.0976461{col 99}{space 3} .9009395
{txt}{space 39}2013  {c |}{col 46}{res}{space 2} .2741895{col 58}{space 2} .2501651{col 69}{space 1}    1.10{col 78}{space 3}0.273{col 86}{space 4}-.2161302{col 99}{space 3} .7645091
{txt}{space 39}2014  {c |}{col 46}{res}{space 2} .4171016{col 58}{space 2} .2751698{col 69}{space 1}    1.52{col 78}{space 3}0.130{col 86}{space 4}-.1222269{col 99}{space 3} .9564302
{txt}{space 39}2015  {c |}{col 46}{res}{space 2} .5703105{col 58}{space 2} .2799007{col 69}{space 1}    2.04{col 78}{space 3}0.042{col 86}{space 4} .0217094{col 99}{space 3} 1.118912
{txt}{space 39}2016  {c |}{col 46}{res}{space 2} .3213849{col 58}{space 2} .2542516{col 69}{space 1}    1.26{col 78}{space 3}0.206{col 86}{space 4}-.1769444{col 99}{space 3} .8197141
{txt}{space 39}2017  {c |}{col 46}{res}{space 2} .4789811{col 58}{space 2} .2623426{col 69}{space 1}    1.83{col 78}{space 3}0.068{col 86}{space 4}-.0352063{col 99}{space 3} .9931685
{txt}{space 39}2018  {c |}{col 46}{res}{space 2} .3028665{col 58}{space 2}  .269236{col 69}{space 1}    1.12{col 78}{space 3}0.261{col 86}{space 4} -.224832{col 99}{space 3}  .830565
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.1960356{col 58}{space 2} .2194846{col 69}{space 1}   -0.89{col 78}{space 3}0.372{col 86}{space 4}-.6262219{col 99}{space 3} .2341507
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}-.0787398{col 58}{space 2} .2259495{col 69}{space 1}   -0.35{col 78}{space 3}0.727{col 86}{space 4}-.5215973{col 99}{space 3} .3641176
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.1980976{col 58}{space 2} .2467654{col 69}{space 1}   -0.80{col 78}{space 3}0.422{col 86}{space 4} -.681754{col 99}{space 3} .2855589
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.1310004{col 58}{space 2} .2173196{col 69}{space 1}   -0.60{col 78}{space 3}0.547{col 86}{space 4}-.5569435{col 99}{space 3} .2949426
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.1283624{col 58}{space 2} .0410918{col 69}{space 1}   -3.12{col 78}{space 3}0.002{col 86}{space 4}-.2089017{col 99}{space 3}-.0478231
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2} .0194303{col 58}{space 2} .0293001{col 69}{space 1}    0.66{col 78}{space 3}0.507{col 86}{space 4}-.0379974{col 99}{space 3} .0768579
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0759608{col 58}{space 2} .0218855{col 69}{space 1}    3.47{col 78}{space 3}0.001{col 86}{space 4} .0330655{col 99}{space 3} .1188561
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0567608{col 58}{space 2} .0240433{col 69}{space 1}    2.36{col 78}{space 3}0.018{col 86}{space 4} .0096363{col 99}{space 3} .1038854
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2} -.009517{col 58}{space 2} .0275021{col 69}{space 1}   -0.35{col 78}{space 3}0.729{col 86}{space 4}-.0634207{col 99}{space 3} .0443867
{txt}{space 38}avila  {c |}{col 46}{res}{space 2} -.011769{col 58}{space 2} .0412227{col 69}{space 1}   -0.29{col 78}{space 3}0.775{col 86}{space 4}-.0925648{col 99}{space 3} .0690269
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0831772{col 58}{space 2} .0253656{col 69}{space 1}    3.28{col 78}{space 3}0.001{col 86}{space 4}  .033461{col 99}{space 3} .1328934
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.0338954{col 58}{space 2} .0251293{col 69}{space 1}   -1.35{col 78}{space 3}0.177{col 86}{space 4}-.0831484{col 99}{space 3} .0153576
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0310099{col 58}{space 2} .0384294{col 69}{space 1}    0.81{col 78}{space 3}0.420{col 86}{space 4}-.0443112{col 99}{space 3} .1063309
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2} .1109826{col 58}{space 2} .0299778{col 69}{space 1}    3.70{col 78}{space 3}0.000{col 86}{space 4} .0522265{col 99}{space 3} .1697386
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2} .0085712{col 58}{space 2} .0245458{col 69}{space 1}    0.35{col 78}{space 3}0.727{col 86}{space 4}-.0395381{col 99}{space 3} .0566806
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2} .0011476{col 58}{space 2} .0258221{col 69}{space 1}    0.04{col 78}{space 3}0.965{col 86}{space 4}-.0494632{col 99}{space 3} .0517585
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2} .0404821{col 58}{space 2} .0261179{col 69}{space 1}    1.55{col 78}{space 3}0.121{col 86}{space 4}-.0107087{col 99}{space 3} .0916729
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0455257{col 58}{space 2} .0297416{col 69}{space 1}    1.53{col 78}{space 3}0.126{col 86}{space 4}-.0127673{col 99}{space 3} .1038187
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2} .0225098{col 58}{space 2} .0236204{col 69}{space 1}    0.95{col 78}{space 3}0.341{col 86}{space 4}-.0237858{col 99}{space 3} .0688053
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2} .0395923{col 58}{space 2} .0371223{col 69}{space 1}    1.07{col 78}{space 3}0.286{col 86}{space 4}-.0331669{col 99}{space 3} .1123515
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.1115695{col 58}{space 2} .0320824{col 69}{space 1}   -3.48{col 78}{space 3}0.001{col 86}{space 4}-.1744505{col 99}{space 3}-.0486885
{txt}{space 37}girona  {c |}{col 46}{res}{space 2} -.091401{col 58}{space 2} .0330171{col 69}{space 1}   -2.77{col 78}{space 3}0.006{col 86}{space 4}-.1561141{col 99}{space 3} -.026688
{txt}{space 36}granada  {c |}{col 46}{res}{space 2} .0900618{col 58}{space 2}  .024856{col 69}{space 1}    3.62{col 78}{space 3}0.000{col 86}{space 4} .0413445{col 99}{space 3} .1387792
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0875781{col 58}{space 2} .0332302{col 69}{space 1}    2.64{col 78}{space 3}0.008{col 86}{space 4} .0224474{col 99}{space 3} .1527088
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2} .0430327{col 58}{space 2} .0326832{col 69}{space 1}    1.32{col 78}{space 3}0.188{col 86}{space 4}-.0210258{col 99}{space 3} .1070912
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2} .0151231{col 58}{space 2} .0378958{col 69}{space 1}    0.40{col 78}{space 3}0.690{col 86}{space 4} -.059152{col 99}{space 3} .0893982
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2}-.0213057{col 58}{space 2} .0318658{col 69}{space 1}   -0.67{col 78}{space 3}0.504{col 86}{space 4}-.0837622{col 99}{space 3} .0411508
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0479573{col 58}{space 2} .0251321{col 69}{space 1}    1.91{col 78}{space 3}0.056{col 86}{space 4}-.0013012{col 99}{space 3} .0972157
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0666501{col 58}{space 2} .0292715{col 69}{space 1}    2.28{col 78}{space 3}0.023{col 86}{space 4} .0092784{col 99}{space 3} .1240218
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} .0159108{col 58}{space 2} .0296314{col 69}{space 1}    0.54{col 78}{space 3}0.591{col 86}{space 4}-.0421663{col 99}{space 3}  .073988
{txt}{space 39}leon  {c |}{col 46}{res}{space 2} .0297176{col 58}{space 2} .0311411{col 69}{space 1}    0.95{col 78}{space 3}0.340{col 86}{space 4}-.0313185{col 99}{space 3} .0907537
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2}-.0852642{col 58}{space 2} .0331589{col 69}{space 1}   -2.57{col 78}{space 3}0.010{col 86}{space 4}-.1502551{col 99}{space 3}-.0202732
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2} .0349029{col 58}{space 2} .0288006{col 69}{space 1}    1.21{col 78}{space 3}0.226{col 86}{space 4}-.0215458{col 99}{space 3} .0913517
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2} .0083944{col 58}{space 2} .0222521{col 69}{space 1}    0.38{col 78}{space 3}0.706{col 86}{space 4}-.0352193{col 99}{space 3} .0520082
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0646669{col 58}{space 2} .0219002{col 69}{space 1}    2.95{col 78}{space 3}0.003{col 86}{space 4} .0217429{col 99}{space 3}  .107591
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2} .0360909{col 58}{space 2} .0217804{col 69}{space 1}    1.66{col 78}{space 3}0.098{col 86}{space 4}-.0065984{col 99}{space 3} .0787802
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2} .0076826{col 58}{space 2} .0312726{col 69}{space 1}    0.25{col 78}{space 3}0.806{col 86}{space 4}-.0536112{col 99}{space 3} .0689765
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} .0199494{col 58}{space 2} .0310811{col 69}{space 1}    0.64{col 78}{space 3}0.521{col 86}{space 4}-.0409691{col 99}{space 3} .0808679
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2} .0152218{col 58}{space 2} .0391004{col 69}{space 1}    0.39{col 78}{space 3}0.697{col 86}{space 4}-.0614144{col 99}{space 3} .0918579
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2}-.0148705{col 58}{space 2} .0238326{col 69}{space 1}   -0.62{col 78}{space 3}0.533{col 86}{space 4} -.061582{col 99}{space 3} .0318409
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2} .0362348{col 58}{space 2} .0303069{col 69}{space 1}    1.20{col 78}{space 3}0.232{col 86}{space 4}-.0231662{col 99}{space 3} .0956358
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .0544681{col 58}{space 2} .0247141{col 69}{space 1}    2.20{col 78}{space 3}0.028{col 86}{space 4} .0060288{col 99}{space 3} .1029074
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2} .0818252{col 58}{space 2} .0577691{col 69}{space 1}    1.42{col 78}{space 3}0.157{col 86}{space 4}-.0314013{col 99}{space 3} .1950518
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2} .0255287{col 58}{space 2} .0245551{col 69}{space 1}    1.04{col 78}{space 3}0.299{col 86}{space 4}-.0225989{col 99}{space 3} .0736562
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .0450838{col 58}{space 2} .0932702{col 69}{space 1}    0.48{col 78}{space 3}0.629{col 86}{space 4}-.1377244{col 99}{space 3}  .227892
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2}-.0522685{col 58}{space 2} .0323769{col 69}{space 1}   -1.61{col 78}{space 3}0.106{col 86}{space 4}-.1157267{col 99}{space 3} .0111897
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2}-.0363312{col 58}{space 2} .0400414{col 69}{space 1}   -0.91{col 78}{space 3}0.364{col 86}{space 4}-.1148117{col 99}{space 3} .0421492
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2} .0459034{col 58}{space 2} .0249087{col 69}{space 1}    1.84{col 78}{space 3}0.065{col 86}{space 4}-.0029172{col 99}{space 3}  .094724
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2} .0400682{col 58}{space 2} .0202783{col 69}{space 1}    1.98{col 78}{space 3}0.048{col 86}{space 4}  .000323{col 99}{space 3} .0798133
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2}-.0154041{col 58}{space 2} .0275981{col 69}{space 1}   -0.56{col 78}{space 3}0.577{col 86}{space 4} -.069496{col 99}{space 3} .0386878
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.0903825{col 58}{space 2} .0242042{col 69}{space 1}   -3.73{col 78}{space 3}0.000{col 86}{space 4}-.1378223{col 99}{space 3}-.0429428
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2} .0970519{col 58}{space 2} .0415155{col 69}{space 1}    2.34{col 78}{space 3}0.019{col 86}{space 4} .0156822{col 99}{space 3} .1784216
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2}  .023834{col 58}{space 2} .0254184{col 69}{space 1}    0.94{col 78}{space 3}0.348{col 86}{space 4}-.0259857{col 99}{space 3} .0736537
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2}-.0009522{col 58}{space 2} .0102543{col 69}{space 1}   -0.09{col 78}{space 3}0.926{col 86}{space 4}-.0210506{col 99}{space 3} .0191461
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} -.002626{col 58}{space 2} .0100284{col 69}{space 1}   -0.26{col 78}{space 3}0.793{col 86}{space 4}-.0222814{col 99}{space 3} .0170295
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2}-.0131487{col 58}{space 2} .0117155{col 69}{space 1}   -1.12{col 78}{space 3}0.262{col 86}{space 4}-.0361109{col 99}{space 3} .0098134
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0028622{col 58}{space 2} .0106355{col 69}{space 1}    0.27{col 78}{space 3}0.788{col 86}{space 4}-.0179832{col 99}{space 3} .0237075
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0063327{col 58}{space 2} .0152695{col 69}{space 1}   -0.41{col 78}{space 3}0.678{col 86}{space 4}-.0362608{col 99}{space 3} .0235953
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}  .007474{col 58}{space 2}  .016348{col 69}{space 1}    0.46{col 78}{space 3}0.648{col 86}{space 4}-.0245678{col 99}{space 3} .0395158
{txt}{space 44} {c |}
{space 38}female {c |}{col 46}{res}{space 2}-.0293175{col 58}{space 2} .0036779{col 69}{space 1}   -7.97{col 78}{space 3}0.000{col 86}{space 4}-.0365262{col 99}{space 3}-.0221089
{txt}{space 41}age {c |}{col 46}{res}{space 2} .0018824{col 58}{space 2} .0002292{col 69}{space 1}    8.21{col 78}{space 3}0.000{col 86}{space 4} .0014331{col 99}{space 3} .0023317
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0171608{col 58}{space 2} .0078476{col 69}{space 1}    2.19{col 78}{space 3}0.029{col 86}{space 4} .0017796{col 99}{space 3}  .032542
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0454215{col 58}{space 2} .0087912{col 69}{space 1}    5.17{col 78}{space 3}0.000{col 86}{space 4}  .028191{col 99}{space 3} .0626521
{txt}{space 36}Student  {c |}{col 46}{res}{space 2} .0053743{col 58}{space 2} .0204187{col 69}{space 1}    0.26{col 78}{space 3}0.792{col 86}{space 4} -.034646{col 99}{space 3} .0453946
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2} .0356633{col 58}{space 2} .0104875{col 69}{space 1}    3.40{col 78}{space 3}0.001{col 86}{space 4} .0151078{col 99}{space 3} .0562187
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2} .0390948{col 58}{space 2} .0188405{col 69}{space 1}    2.08{col 78}{space 3}0.038{col 86}{space 4} .0021676{col 99}{space 3} .0760219
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0374281{col 58}{space 2} .0174117{col 69}{space 1}    2.15{col 78}{space 3}0.032{col 86}{space 4} .0033015{col 99}{space 3} .0715546
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0364012{col 58}{space 2} .0168427{col 69}{space 1}    2.16{col 78}{space 3}0.031{col 86}{space 4} .0033897{col 99}{space 3} .0694127
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0242482{col 58}{space 2}  .017971{col 69}{space 1}    1.35{col 78}{space 3}0.177{col 86}{space 4}-.0109747{col 99}{space 3}  .059471
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0293624{col 58}{space 2} .0164756{col 69}{space 1}    1.78{col 78}{space 3}0.075{col 86}{space 4}-.0029295{col 99}{space 3} .0616543
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0004281{col 58}{space 2} .0221769{col 69}{space 1}   -0.02{col 78}{space 3}0.985{col 86}{space 4}-.0438944{col 99}{space 3} .0430382
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#female {c |}
{space 40}0 1  {c |}{col 46}{res}{space 2} .0195116{col 58}{space 2} .0065696{col 69}{space 1}    2.97{col 78}{space 3}0.003{col 86}{space 4} .0066353{col 99}{space 3} .0323879
{txt}{space 40}1 0  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 40}1 1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2}-.0010814{col 58}{space 2}  .000259{col 69}{space 1}   -4.18{col 78}{space 3}0.000{col 86}{space 4}-.0015889{col 99}{space 3}-.0005738
{txt}{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2} .0187248{col 58}{space 2} .0115827{col 69}{space 1}    1.62{col 78}{space 3}0.106{col 86}{space 4}-.0039771{col 99}{space 3} .0414266
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2}-.0023277{col 58}{space 2} .0128746{col 69}{space 1}   -0.18{col 78}{space 3}0.857{col 86}{space 4}-.0275618{col 99}{space 3} .0229064
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2} .0049688{col 58}{space 2} .0106464{col 69}{space 1}    0.47{col 78}{space 3}0.641{col 86}{space 4} -.015898{col 99}{space 3} .0258356
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2}-.0243943{col 58}{space 2} .0261511{col 69}{space 1}   -0.93{col 78}{space 3}0.351{col 86}{space 4}-.0756501{col 99}{space 3} .0268615
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2} .0199096{col 58}{space 2} .0297144{col 69}{space 1}    0.67{col 78}{space 3}0.503{col 86}{space 4}-.0383302{col 99}{space 3} .0781494
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2} .0605373{col 58}{space 2} .0467398{col 69}{space 1}    1.30{col 78}{space 3}0.195{col 86}{space 4} -.031072{col 99}{space 3} .1521466
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .0808689{col 58}{space 2} .0365249{col 69}{space 1}    2.21{col 78}{space 3}0.027{col 86}{space 4} .0092807{col 99}{space 3}  .152457
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0416174{col 58}{space 2} .0296964{col 69}{space 1}    1.40{col 78}{space 3}0.161{col 86}{space 4}-.0165871{col 99}{space 3}  .099822
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0343617{col 58}{space 2} .0321697{col 69}{space 1}    1.07{col 78}{space 3}0.285{col 86}{space 4}-.0286903{col 99}{space 3} .0974137
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2}  .024194{col 58}{space 2} .0347173{col 69}{space 1}    0.70{col 78}{space 3}0.486{col 86}{space 4}-.0438513{col 99}{space 3} .0922394
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0946922{col 58}{space 2} .0482932{col 69}{space 1}    1.96{col 78}{space 3}0.050{col 86}{space 4} .0000382{col 99}{space 3} .1893462
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0487281{col 58}{space 2} .0324676{col 69}{space 1}    1.50{col 78}{space 3}0.133{col 86}{space 4}-.0149079{col 99}{space 3} .1123641
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} .0619277{col 58}{space 2} .0300248{col 69}{space 1}    2.06{col 78}{space 3}0.039{col 86}{space 4} .0030796{col 99}{space 3} .1207757
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0023397{col 58}{space 2} .0452021{col 69}{space 1}   -0.05{col 78}{space 3}0.959{col 86}{space 4}-.0909351{col 99}{space 3} .0862557
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2} .0075038{col 58}{space 2} .0370387{col 69}{space 1}    0.20{col 78}{space 3}0.839{col 86}{space 4}-.0650915{col 99}{space 3} .0800991
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .1003749{col 58}{space 2} .0319961{col 69}{space 1}    3.14{col 78}{space 3}0.002{col 86}{space 4} .0376629{col 99}{space 3} .1630868
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .1076429{col 58}{space 2} .0338631{col 69}{space 1}    3.18{col 78}{space 3}0.001{col 86}{space 4} .0412718{col 99}{space 3} .1740141
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2}  .062352{col 58}{space 2} .0332574{col 69}{space 1}    1.87{col 78}{space 3}0.061{col 86}{space 4}-.0028321{col 99}{space 3}  .127536
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2} .0306098{col 58}{space 2} .0369242{col 69}{space 1}    0.83{col 78}{space 3}0.407{col 86}{space 4}-.0417611{col 99}{space 3} .1029807
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2}-.0338578{col 58}{space 2} .0311617{col 69}{space 1}   -1.09{col 78}{space 3}0.277{col 86}{space 4}-.0949343{col 99}{space 3} .0272187
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2} .0346264{col 58}{space 2} .0442301{col 69}{space 1}    0.78{col 78}{space 3}0.434{col 86}{space 4}-.0520639{col 99}{space 3} .1213167
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0310675{col 58}{space 2} .0376002{col 69}{space 1}   -0.83{col 78}{space 3}0.409{col 86}{space 4}-.1047633{col 99}{space 3} .0426284
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2} .0495286{col 58}{space 2} .0386008{col 69}{space 1}    1.28{col 78}{space 3}0.199{col 86}{space 4}-.0261285{col 99}{space 3} .1251856
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2}-.0359109{col 58}{space 2} .0326194{col 69}{space 1}   -1.10{col 78}{space 3}0.271{col 86}{space 4}-.0998445{col 99}{space 3} .0280226
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2}-.0067031{col 58}{space 2} .0409539{col 69}{space 1}   -0.16{col 78}{space 3}0.870{col 86}{space 4}-.0869721{col 99}{space 3}  .073566
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} .0546458{col 58}{space 2} .0391594{col 69}{space 1}    1.40{col 78}{space 3}0.163{col 86}{space 4}-.0221061{col 99}{space 3} .1313977
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2}-.0144841{col 58}{space 2} .0450245{col 69}{space 1}   -0.32{col 78}{space 3}0.748{col 86}{space 4}-.1027315{col 99}{space 3} .0737633
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2}  .098282{col 58}{space 2}  .036767{col 69}{space 1}    2.67{col 78}{space 3}0.008{col 86}{space 4} .0262192{col 99}{space 3} .1703447
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2} .0161008{col 58}{space 2}  .032773{col 69}{space 1}    0.49{col 78}{space 3}0.623{col 86}{space 4}-.0481338{col 99}{space 3} .0803355
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0643416{col 58}{space 2} .0379098{col 69}{space 1}   -1.70{col 78}{space 3}0.090{col 86}{space 4}-.1386442{col 99}{space 3}  .009961
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .1000625{col 58}{space 2} .0356881{col 69}{space 1}    2.80{col 78}{space 3}0.005{col 86}{space 4} .0301143{col 99}{space 3} .1700106
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2} .0079623{col 58}{space 2} .0382567{col 69}{space 1}    0.21{col 78}{space 3}0.835{col 86}{space 4}-.0670202{col 99}{space 3} .0829448
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2} .0160894{col 58}{space 2} .0408987{col 69}{space 1}    0.39{col 78}{space 3}0.694{col 86}{space 4}-.0640713{col 99}{space 3} .0962502
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2}   .05363{col 58}{space 2} .0364174{col 69}{space 1}    1.47{col 78}{space 3}0.141{col 86}{space 4}-.0177476{col 99}{space 3} .1250077
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0303483{col 58}{space 2} .0283058{col 69}{space 1}    1.07{col 78}{space 3}0.284{col 86}{space 4}-.0251307{col 99}{space 3} .0858273
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .0616024{col 58}{space 2} .0275504{col 69}{space 1}    2.24{col 78}{space 3}0.025{col 86}{space 4} .0076041{col 99}{space 3} .1156008
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2} .0328004{col 58}{space 2} .0283975{col 69}{space 1}    1.16{col 78}{space 3}0.248{col 86}{space 4}-.0228583{col 99}{space 3}  .088459
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2}-.0138497{col 58}{space 2} .0373427{col 69}{space 1}   -0.37{col 78}{space 3}0.711{col 86}{space 4}-.0870408{col 99}{space 3} .0593414
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2} .0609161{col 58}{space 2} .0373993{col 69}{space 1}    1.63{col 78}{space 3}0.103{col 86}{space 4}-.0123858{col 99}{space 3} .1342181
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2} .0158615{col 58}{space 2} .0467869{col 69}{space 1}    0.34{col 78}{space 3}0.735{col 86}{space 4}-.0758401{col 99}{space 3} .1075632
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2} .0150333{col 58}{space 2} .0307753{col 69}{space 1}    0.49{col 78}{space 3}0.625{col 86}{space 4}-.0452859{col 99}{space 3} .0753524
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .1083023{col 58}{space 2} .0377139{col 69}{space 1}    2.87{col 78}{space 3}0.004{col 86}{space 4} .0343837{col 99}{space 3}  .182221
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0069484{col 58}{space 2} .0320473{col 69}{space 1}   -0.22{col 78}{space 3}0.828{col 86}{space 4}-.0697606{col 99}{space 3} .0558638
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0409792{col 58}{space 2} .0640391{col 69}{space 1}   -0.64{col 78}{space 3}0.522{col 86}{space 4}-.1664948{col 99}{space 3} .0845365
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .0891232{col 58}{space 2}  .029442{col 69}{space 1}    3.03{col 78}{space 3}0.002{col 86}{space 4} .0314173{col 99}{space 3} .1468291
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2} .0251926{col 58}{space 2}  .099052{col 69}{space 1}    0.25{col 78}{space 3}0.799{col 86}{space 4}-.1689478{col 99}{space 3}  .219333
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2}  .079721{col 58}{space 2} .0378089{col 69}{space 1}    2.11{col 78}{space 3}0.035{col 86}{space 4} .0056161{col 99}{space 3} .1538259
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0946809{col 58}{space 2}  .046354{col 69}{space 1}    2.04{col 78}{space 3}0.041{col 86}{space 4} .0038278{col 99}{space 3} .1855341
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2} .0178216{col 58}{space 2} .0329844{col 69}{space 1}    0.54{col 78}{space 3}0.589{col 86}{space 4}-.0468273{col 99}{space 3} .0824705
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2} .0416856{col 58}{space 2} .0264161{col 69}{space 1}    1.58{col 78}{space 3}0.115{col 86}{space 4}-.0100896{col 99}{space 3} .0934607
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2}  .090202{col 58}{space 2} .0351735{col 69}{space 1}    2.56{col 78}{space 3}0.010{col 86}{space 4} .0212624{col 99}{space 3} .1591416
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0708561{col 58}{space 2} .0310916{col 69}{space 1}   -2.28{col 78}{space 3}0.023{col 86}{space 4}-.1317951{col 99}{space 3}-.0099171
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2} .0150036{col 58}{space 2} .0483163{col 69}{space 1}    0.31{col 78}{space 3}0.756{col 86}{space 4}-.0796956{col 99}{space 3} .1097028
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1190782{col 58}{space 2} .0341433{col 69}{space 1}    3.49{col 78}{space 3}0.000{col 86}{space 4} .0521579{col 99}{space 3} .1859985
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}116152{txt}) = {res}10.674{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.0550
{col 25}{txt}Prob>|t| = {res}    0.9409

95%{txt} confidence set for null hypothesis expression: {res}[−.01625, .01722]

{txt}{col 1}Mean estimation{col 42}{lalign 13:Number of obs}{col 55} = {res}{ralign 7:116,367}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2814801{col 28}{space 2} .0013183{col 39}{space 5} .2788962{col 53}{space 3} .2840641
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_24_25_26_primary_or_below.tex"'})
(1,035,151 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:36,822}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:85}, {res:36702})}{col 70} = {res}{ralign 6:14.31}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0515}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0484}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4184}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0080683{col 56}{space 2} .0081102{col 67}{space 1}    0.99{col 76}{space 3}0.320{col 84}{space 4} -.007828{col 97}{space 3} .0239646
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .3370936{col 56}{space 2} .2937703{col 67}{space 1}    1.15{col 76}{space 3}0.251{col 84}{space 4}-.2387046{col 97}{space 3} .9128918
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2} .2306058{col 56}{space 2} .3939459{col 67}{space 1}    0.59{col 76}{space 3}0.558{col 84}{space 4}-.5415394{col 97}{space 3} 1.002751
{txt}{space 37}1989  {c |}{col 44}{res}{space 2}-.2256827{col 56}{space 2} .3355507{col 67}{space 1}   -0.67{col 76}{space 3}0.501{col 84}{space 4}-.8833717{col 97}{space 3} .4320063
{txt}{space 37}1990  {c |}{col 44}{res}{space 2} -.499419{col 56}{space 2} .3360946{col 67}{space 1}   -1.49{col 76}{space 3}0.137{col 84}{space 4}-1.158174{col 97}{space 3} .1593361
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.6325055{col 56}{space 2} .4095805{col 67}{space 1}   -1.54{col 76}{space 3}0.123{col 84}{space 4}-1.435295{col 97}{space 3} .1702841
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.5931559{col 56}{space 2} .3396757{col 67}{space 1}   -1.75{col 76}{space 3}0.081{col 84}{space 4} -1.25893{col 97}{space 3} .0726181
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}-.3236472{col 56}{space 2} .3245614{col 67}{space 1}   -1.00{col 76}{space 3}0.319{col 84}{space 4}-.9597969{col 97}{space 3} .3125025
{txt}{space 37}1994  {c |}{col 44}{res}{space 2}-.1354234{col 56}{space 2} .3249822{col 67}{space 1}   -0.42{col 76}{space 3}0.677{col 84}{space 4}-.7723979{col 97}{space 3}  .501551
{txt}{space 37}1995  {c |}{col 44}{res}{space 2}-.1621378{col 56}{space 2} .3218253{col 67}{space 1}   -0.50{col 76}{space 3}0.614{col 84}{space 4}-.7929245{col 97}{space 3} .4686489
{txt}{space 37}1996  {c |}{col 44}{res}{space 2}-.2524421{col 56}{space 2} .3358533{col 67}{space 1}   -0.75{col 76}{space 3}0.452{col 84}{space 4}-.9107241{col 97}{space 3} .4058399
{txt}{space 37}1997  {c |}{col 44}{res}{space 2} .0253099{col 56}{space 2}  .374151{col 67}{space 1}    0.07{col 76}{space 3}0.946{col 84}{space 4}-.7080368{col 97}{space 3} .7586566
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .3583376{col 56}{space 2} .3894193{col 67}{space 1}    0.92{col 76}{space 3}0.357{col 84}{space 4}-.4049354{col 97}{space 3} 1.121611
{txt}{space 37}1999  {c |}{col 44}{res}{space 2}-.2616856{col 56}{space 2} .3806064{col 67}{space 1}   -0.69{col 76}{space 3}0.492{col 84}{space 4}-1.007685{col 97}{space 3} .4843138
{txt}{space 37}2000  {c |}{col 44}{res}{space 2} .0199119{col 56}{space 2} .2966017{col 67}{space 1}    0.07{col 76}{space 3}0.946{col 84}{space 4} -.561436{col 97}{space 3} .6012597
{txt}{space 37}2001  {c |}{col 44}{res}{space 2}-.4261317{col 56}{space 2} .3540904{col 67}{space 1}   -1.20{col 76}{space 3}0.229{col 84}{space 4}-1.120159{col 97}{space 3} .2678955
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} .1304461{col 56}{space 2} .3631301{col 67}{space 1}    0.36{col 76}{space 3}0.719{col 84}{space 4}-.5812994{col 97}{space 3} .8421916
{txt}{space 37}2003  {c |}{col 44}{res}{space 2}-.5894298{col 56}{space 2} .3581063{col 67}{space 1}   -1.65{col 76}{space 3}0.100{col 84}{space 4}-1.291328{col 97}{space 3} .1124689
{txt}{space 37}2004  {c |}{col 44}{res}{space 2}-.0774319{col 56}{space 2} .2951184{col 67}{space 1}   -0.26{col 76}{space 3}0.793{col 84}{space 4}-.6558723{col 97}{space 3} .5010086
{txt}{space 37}2005  {c |}{col 44}{res}{space 2}-.8364619{col 56}{space 2} .3604238{col 67}{space 1}   -2.32{col 76}{space 3}0.020{col 84}{space 4}-1.542903{col 97}{space 3} -.130021
{txt}{space 37}2006  {c |}{col 44}{res}{space 2}-.6899181{col 56}{space 2} .3384449{col 67}{space 1}   -2.04{col 76}{space 3}0.042{col 84}{space 4} -1.35328{col 97}{space 3}-.0265564
{txt}{space 37}2007  {c |}{col 44}{res}{space 2} -.452897{col 56}{space 2}   .32366{col 67}{space 1}   -1.40{col 76}{space 3}0.162{col 84}{space 4} -1.08728{col 97}{space 3}  .181486
{txt}{space 37}2008  {c |}{col 44}{res}{space 2}-.2769433{col 56}{space 2}  .293545{col 67}{space 1}   -0.94{col 76}{space 3}0.345{col 84}{space 4}-.8522999{col 97}{space 3} .2984133
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}-.7248643{col 56}{space 2} .3286604{col 67}{space 1}   -2.21{col 76}{space 3}0.027{col 84}{space 4}-1.369048{col 97}{space 3}-.0806806
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1266617{col 56}{space 2} .0222746{col 67}{space 1}   -5.69{col 76}{space 3}0.000{col 84}{space 4}-.1703205{col 97}{space 3}-.0830028
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0298627{col 56}{space 2} .0258274{col 67}{space 1}    1.16{col 76}{space 3}0.248{col 84}{space 4}-.0207598{col 97}{space 3} .0804851
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0789621{col 56}{space 2} .0196402{col 67}{space 1}    4.02{col 76}{space 3}0.000{col 84}{space 4} .0404667{col 97}{space 3} .1174576
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0663953{col 56}{space 2} .0243208{col 67}{space 1}    2.73{col 76}{space 3}0.006{col 84}{space 4} .0187259{col 97}{space 3} .1140648
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0222933{col 56}{space 2}  .018778{col 67}{space 1}    1.19{col 76}{space 3}0.235{col 84}{space 4} -.014512{col 97}{space 3} .0590987
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0034876{col 56}{space 2} .0311462{col 67}{space 1}    0.11{col 76}{space 3}0.911{col 84}{space 4}-.0575598{col 97}{space 3} .0645349
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2}  .069431{col 56}{space 2} .0242421{col 67}{space 1}    2.86{col 76}{space 3}0.004{col 84}{space 4} .0219157{col 97}{space 3} .1169462
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0444659{col 56}{space 2} .0157818{col 67}{space 1}   -2.82{col 76}{space 3}0.005{col 84}{space 4}-.0753988{col 97}{space 3}-.0135331
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2}-.0134667{col 56}{space 2} .0234102{col 67}{space 1}   -0.58{col 76}{space 3}0.565{col 84}{space 4}-.0593513{col 97}{space 3} .0324179
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0650756{col 56}{space 2}  .025072{col 67}{space 1}    2.60{col 76}{space 3}0.009{col 84}{space 4} .0159339{col 97}{space 3} .1142174
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0687762{col 56}{space 2} .0222268{col 67}{space 1}    3.09{col 76}{space 3}0.002{col 84}{space 4}  .025211{col 97}{space 3} .1123415
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0695017{col 56}{space 2} .0210938{col 67}{space 1}    3.29{col 76}{space 3}0.001{col 84}{space 4} .0281572{col 97}{space 3} .1108462
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0880397{col 56}{space 2} .0234448{col 67}{space 1}    3.76{col 76}{space 3}0.000{col 84}{space 4} .0420873{col 97}{space 3} .1339921
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0621165{col 56}{space 2} .0257354{col 67}{space 1}    2.41{col 76}{space 3}0.016{col 84}{space 4} .0116744{col 97}{space 3} .1125585
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0286468{col 56}{space 2} .0210619{col 67}{space 1}   -1.36{col 76}{space 3}0.174{col 84}{space 4}-.0699286{col 97}{space 3} .0126351
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0084346{col 56}{space 2} .0306776{col 67}{space 1}    0.27{col 76}{space 3}0.783{col 84}{space 4}-.0516944{col 97}{space 3} .0685636
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2} -.186806{col 56}{space 2} .0193153{col 67}{space 1}   -9.67{col 76}{space 3}0.000{col 84}{space 4}-.2246646{col 97}{space 3}-.1489474
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0814847{col 56}{space 2} .0224042{col 67}{space 1}   -3.64{col 76}{space 3}0.000{col 84}{space 4}-.1253975{col 97}{space 3}-.0375719
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0099843{col 56}{space 2} .0215051{col 67}{space 1}    0.46{col 76}{space 3}0.642{col 84}{space 4}-.0321662{col 97}{space 3} .0521348
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2}-.0091481{col 56}{space 2} .0274664{col 67}{space 1}   -0.33{col 76}{space 3}0.739{col 84}{space 4}-.0629831{col 97}{space 3} .0446869
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0685235{col 56}{space 2} .0277756{col 67}{space 1}    2.47{col 76}{space 3}0.014{col 84}{space 4} .0140824{col 97}{space 3} .1229645
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2}-.0107568{col 56}{space 2} .0262465{col 67}{space 1}   -0.41{col 76}{space 3}0.682{col 84}{space 4}-.0622006{col 97}{space 3}  .040687
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0624792{col 56}{space 2} .0215071{col 67}{space 1}    2.91{col 76}{space 3}0.004{col 84}{space 4} .0203247{col 97}{space 3} .1046336
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2}  .016852{col 56}{space 2}  .022442{col 67}{space 1}    0.75{col 76}{space 3}0.453{col 84}{space 4} -.027135{col 97}{space 3}  .060839
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2}-.0434247{col 56}{space 2} .0236451{col 67}{space 1}   -1.84{col 76}{space 3}0.066{col 84}{space 4}-.0897698{col 97}{space 3} .0029204
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0733478{col 56}{space 2} .0220669{col 67}{space 1}    3.32{col 76}{space 3}0.001{col 84}{space 4} .0300961{col 97}{space 3} .1165995
{txt}{space 37}leon  {c |}{col 44}{res}{space 2}-.0212502{col 56}{space 2} .0227529{col 67}{space 1}   -0.93{col 76}{space 3}0.350{col 84}{space 4}-.0658464{col 97}{space 3} .0233461
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.1337061{col 56}{space 2} .0269609{col 67}{space 1}   -4.96{col 76}{space 3}0.000{col 84}{space 4}-.1865502{col 97}{space 3} -.080862
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}-.0090768{col 56}{space 2} .0239681{col 67}{space 1}   -0.38{col 76}{space 3}0.705{col 84}{space 4} -.056055{col 97}{space 3} .0379014
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2}-.0003001{col 56}{space 2}  .016286{col 67}{space 1}   -0.02{col 76}{space 3}0.985{col 84}{space 4} -.032221{col 97}{space 3} .0316209
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0885521{col 56}{space 2} .0201957{col 67}{space 1}    4.38{col 76}{space 3}0.000{col 84}{space 4}  .048968{col 97}{space 3} .1281362
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0392426{col 56}{space 2} .0198684{col 67}{space 1}    1.98{col 76}{space 3}0.048{col 84}{space 4} .0002998{col 97}{space 3} .0781853
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0788154{col 56}{space 2} .0223551{col 67}{space 1}   -3.53{col 76}{space 3}0.000{col 84}{space 4}-.1226321{col 97}{space 3}-.0349987
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2}-.0039067{col 56}{space 2} .0239108{col 67}{space 1}   -0.16{col 76}{space 3}0.870{col 84}{space 4}-.0507725{col 97}{space 3} .0429592
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0607689{col 56}{space 2} .0290373{col 67}{space 1}    2.09{col 76}{space 3}0.036{col 84}{space 4} .0038549{col 97}{space 3} .1176829
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}-.0225715{col 56}{space 2} .0199087{col 67}{space 1}   -1.13{col 76}{space 3}0.257{col 84}{space 4}-.0615932{col 97}{space 3} .0164502
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0592106{col 56}{space 2} .0257823{col 67}{space 1}    2.30{col 76}{space 3}0.022{col 84}{space 4} .0086765{col 97}{space 3} .1097446
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0467713{col 56}{space 2} .0203565{col 67}{space 1}    2.30{col 76}{space 3}0.022{col 84}{space 4}  .006872{col 97}{space 3} .0866705
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2}-.0275195{col 56}{space 2} .0298808{col 67}{space 1}   -0.92{col 76}{space 3}0.357{col 84}{space 4}-.0860868{col 97}{space 3} .0310477
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0601701{col 56}{space 2} .0209288{col 67}{space 1}    2.87{col 76}{space 3}0.004{col 84}{space 4} .0191491{col 97}{space 3} .1011911
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0374434{col 56}{space 2} .0357723{col 67}{space 1}    1.05{col 76}{space 3}0.295{col 84}{space 4}-.0326713{col 97}{space 3} .1075582
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0225998{col 56}{space 2}  .023153{col 67}{space 1}   -0.98{col 76}{space 3}0.329{col 84}{space 4}-.0679804{col 97}{space 3} .0227808
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0149928{col 56}{space 2} .0269382{col 67}{space 1}    0.56{col 76}{space 3}0.578{col 84}{space 4}-.0378069{col 97}{space 3} .0677925
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0351354{col 56}{space 2} .0237646{col 67}{space 1}    1.48{col 76}{space 3}0.139{col 84}{space 4}-.0114438{col 97}{space 3} .0817147
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0186246{col 56}{space 2} .0183638{col 67}{space 1}    1.01{col 76}{space 3}0.310{col 84}{space 4}-.0173689{col 97}{space 3} .0546182
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0184823{col 56}{space 2} .0231268{col 67}{space 1}    0.80{col 76}{space 3}0.424{col 84}{space 4}-.0268468{col 97}{space 3} .0638114
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2} -.190431{col 56}{space 2} .0176149{col 67}{space 1}  -10.81{col 76}{space 3}0.000{col 84}{space 4}-.2249567{col 97}{space 3}-.1559053
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0582255{col 56}{space 2}  .029541{col 67}{space 1}    1.97{col 76}{space 3}0.049{col 84}{space 4} .0003242{col 97}{space 3} .1161268
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2}-.0168775{col 56}{space 2} .0228632{col 67}{space 1}   -0.74{col 76}{space 3}0.460{col 84}{space 4}-.0616901{col 97}{space 3} .0279351
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} -.010329{col 56}{space 2} .0100267{col 67}{space 1}   -1.03{col 76}{space 3}0.303{col 84}{space 4}-.0299817{col 97}{space 3} .0093236
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0152424{col 56}{space 2} .0097004{col 67}{space 1}   -1.57{col 76}{space 3}0.116{col 84}{space 4}-.0342555{col 97}{space 3} .0037708
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} -.029179{col 56}{space 2} .0110007{col 67}{space 1}   -2.65{col 76}{space 3}0.008{col 84}{space 4}-.0507407{col 97}{space 3}-.0076173
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0024029{col 56}{space 2} .0097708{col 67}{space 1}   -0.25{col 76}{space 3}0.806{col 84}{space 4} -.021554{col 97}{space 3} .0167481
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0560809{col 56}{space 2}  .015232{col 67}{space 1}   -3.68{col 76}{space 3}0.000{col 84}{space 4}-.0859361{col 97}{space 3}-.0262258
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0281265{col 56}{space 2} .0133695{col 67}{space 1}   -2.10{col 76}{space 3}0.035{col 84}{space 4}-.0543311{col 97}{space 3}-.0019219
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}  .002155{col 56}{space 2} .0047255{col 67}{space 1}    0.46{col 76}{space 3}0.648{col 84}{space 4}-.0071071{col 97}{space 3}  .011417
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0015095{col 56}{space 2} .0002237{col 67}{space 1}    6.75{col 76}{space 3}0.000{col 84}{space 4}  .001071{col 97}{space 3} .0019481
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0185479{col 56}{space 2}  .007139{col 67}{space 1}   -2.60{col 76}{space 3}0.009{col 84}{space 4}-.0325405{col 97}{space 3}-.0045553
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0168902{col 56}{space 2} .0113509{col 67}{space 1}    1.49{col 76}{space 3}0.137{col 84}{space 4}-.0053579{col 97}{space 3} .0391383
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0141834{col 56}{space 2} .0070971{col 67}{space 1}   -2.00{col 76}{space 3}0.046{col 84}{space 4}-.0280938{col 97}{space 3}-.0002729
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0039028{col 56}{space 2} .0085397{col 67}{space 1}    0.46{col 76}{space 3}0.648{col 84}{space 4}-.0128352{col 97}{space 3} .0206409
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1723434{col 56}{space 2} .0264145{col 67}{space 1}    6.52{col 76}{space 3}0.000{col 84}{space 4} .1205702{col 97}{space 3} .2241166
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}36702{txt}) = {res}4.444{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.5593
{col 25}{txt}Prob>|t| = {res}    0.6226

95%{txt} confidence set for null hypothesis expression: {res}[−.02871, .04524]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:36,822}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2430884{col 28}{space 2} .0022354{col 39}{space 5} .2387069{col 53}{space 3} .2474698
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:23,270}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:75}, {res:23174})}{col 70} = {res}{ralign 6:6.41}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0340}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0301}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.3805}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0030536{col 56}{space 2} .0088936{col 67}{space 1}   -0.34{col 76}{space 3}0.731{col 84}{space 4}-.0204856{col 97}{space 3} .0143784
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.2868343{col 56}{space 2} .1941665{col 67}{space 1}   -1.48{col 76}{space 3}0.140{col 84}{space 4}-.6674136{col 97}{space 3} .0937449
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2} .1580341{col 56}{space 2} .2549459{col 67}{space 1}    0.62{col 76}{space 3}0.535{col 84}{space 4}-.3416768{col 97}{space 3} .6577449
{txt}{space 37}2012  {c |}{col 44}{res}{space 2}   .71689{col 56}{space 2}  .260963{col 67}{space 1}    2.75{col 76}{space 3}0.006{col 84}{space 4} .2053853{col 97}{space 3} 1.228395
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .3723585{col 56}{space 2} .2437494{col 67}{space 1}    1.53{col 76}{space 3}0.127{col 84}{space 4}-.1054065{col 97}{space 3} .8501236
{txt}{space 37}2014  {c |}{col 44}{res}{space 2} .1606271{col 56}{space 2}  .257376{col 67}{space 1}    0.62{col 76}{space 3}0.533{col 84}{space 4} -.343847{col 97}{space 3} .6651012
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} .2315638{col 56}{space 2} .2472742{col 67}{space 1}    0.94{col 76}{space 3}0.349{col 84}{space 4}  -.25311{col 97}{space 3} .7162376
{txt}{space 37}2016  {c |}{col 44}{res}{space 2} .2788712{col 56}{space 2}  .233553{col 67}{space 1}    1.19{col 76}{space 3}0.232{col 84}{space 4}-.1789082{col 97}{space 3} .7366506
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .3529963{col 56}{space 2} .2322084{col 67}{space 1}    1.52{col 76}{space 3}0.128{col 84}{space 4}-.1021476{col 97}{space 3} .8081403
{txt}{space 37}2018  {c |}{col 44}{res}{space 2}  .232163{col 56}{space 2} .2336887{col 67}{space 1}    0.99{col 76}{space 3}0.320{col 84}{space 4}-.2258823{col 97}{space 3} .6902082
{txt}{space 37}2019  {c |}{col 44}{res}{space 2}-.0459654{col 56}{space 2}  .173427{col 67}{space 1}   -0.27{col 76}{space 3}0.791{col 84}{space 4}-.3858937{col 97}{space 3} .2939629
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .0164529{col 56}{space 2} .1824945{col 67}{space 1}    0.09{col 76}{space 3}0.928{col 84}{space 4}-.3412484{col 97}{space 3} .3741541
{txt}{space 37}2021  {c |}{col 44}{res}{space 2} -.124888{col 56}{space 2} .1988774{col 67}{space 1}   -0.63{col 76}{space 3}0.530{col 84}{space 4}-.5147009{col 97}{space 3}  .264925
{txt}{space 37}2022  {c |}{col 44}{res}{space 2}-.1821551{col 56}{space 2} .1747936{col 67}{space 1}   -1.04{col 76}{space 3}0.297{col 84}{space 4}-.5247622{col 97}{space 3}  .160452
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.0958961{col 56}{space 2} .0357297{col 67}{space 1}   -2.68{col 76}{space 3}0.007{col 84}{space 4}-.1659286{col 97}{space 3}-.0258636
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2}-.0830794{col 56}{space 2}  .032882{col 67}{space 1}   -2.53{col 76}{space 3}0.012{col 84}{space 4}-.1475302{col 97}{space 3}-.0186285
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2}-.0012062{col 56}{space 2} .0226491{col 67}{space 1}   -0.05{col 76}{space 3}0.958{col 84}{space 4}-.0455999{col 97}{space 3} .0431874
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2}-.0271109{col 56}{space 2} .0274215{col 67}{space 1}   -0.99{col 76}{space 3}0.323{col 84}{space 4}-.0808588{col 97}{space 3}  .026637
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2}-.0020471{col 56}{space 2} .0269625{col 67}{space 1}   -0.08{col 76}{space 3}0.939{col 84}{space 4}-.0548954{col 97}{space 3} .0508011
{txt}{space 36}avila  {c |}{col 44}{res}{space 2}-.0519707{col 56}{space 2} .0428643{col 67}{space 1}   -1.21{col 76}{space 3}0.225{col 84}{space 4}-.1359876{col 97}{space 3} .0320462
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2}-.0169823{col 56}{space 2} .0267938{col 67}{space 1}   -0.63{col 76}{space 3}0.526{col 84}{space 4}   -.0695{col 97}{space 3} .0355354
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.1107542{col 56}{space 2} .0243042{col 67}{space 1}   -4.56{col 76}{space 3}0.000{col 84}{space 4}-.1583921{col 97}{space 3}-.0631163
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0176987{col 56}{space 2} .0358417{col 67}{space 1}    0.49{col 76}{space 3}0.621{col 84}{space 4}-.0525533{col 97}{space 3} .0879508
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0292882{col 56}{space 2} .0356005{col 67}{space 1}    0.82{col 76}{space 3}0.411{col 84}{space 4}-.0404912{col 97}{space 3} .0990676
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2}  -.04554{col 56}{space 2} .0246398{col 67}{space 1}   -1.85{col 76}{space 3}0.065{col 84}{space 4}-.0938356{col 97}{space 3} .0027557
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2}-.0447065{col 56}{space 2} .0245006{col 67}{space 1}   -1.82{col 76}{space 3}0.068{col 84}{space 4}-.0927293{col 97}{space 3} .0033163
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} -.030695{col 56}{space 2} .0272457{col 67}{space 1}   -1.13{col 76}{space 3}0.260{col 84}{space 4}-.0840983{col 97}{space 3} .0227084
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2}-.0450753{col 56}{space 2} .0345599{col 67}{space 1}   -1.30{col 76}{space 3}0.192{col 84}{space 4}-.1128151{col 97}{space 3} .0226644
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0503214{col 56}{space 2} .0256979{col 67}{space 1}   -1.96{col 76}{space 3}0.050{col 84}{space 4}-.1006911{col 97}{space 3} .0000482
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .0176337{col 56}{space 2} .0466723{col 67}{space 1}    0.38{col 76}{space 3}0.706{col 84}{space 4}-.0738471{col 97}{space 3} .1091144
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1687038{col 56}{space 2} .0278486{col 67}{space 1}   -6.06{col 76}{space 3}0.000{col 84}{space 4}-.2232889{col 97}{space 3}-.1141187
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.1484761{col 56}{space 2} .0319145{col 67}{space 1}   -4.65{col 76}{space 3}0.000{col 84}{space 4}-.2110306{col 97}{space 3}-.0859216
{txt}{space 34}granada  {c |}{col 44}{res}{space 2}-.0043625{col 56}{space 2} .0258036{col 67}{space 1}   -0.17{col 76}{space 3}0.866{col 84}{space 4}-.0549392{col 97}{space 3} .0462142
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} 8.07e-06{col 56}{space 2} .0319239{col 67}{space 1}    0.00{col 76}{space 3}1.000{col 84}{space 4}-.0625649{col 97}{space 3}  .062581
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2}-.0301604{col 56}{space 2} .0342755{col 67}{space 1}   -0.88{col 76}{space 3}0.379{col 84}{space 4}-.0973426{col 97}{space 3} .0370218
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} -.026162{col 56}{space 2} .0379033{col 67}{space 1}   -0.69{col 76}{space 3}0.490{col 84}{space 4}-.1004549{col 97}{space 3} .0481309
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2}-.0403059{col 56}{space 2}  .030943{col 67}{space 1}   -1.30{col 76}{space 3}0.193{col 84}{space 4}-.1009562{col 97}{space 3} .0203444
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2}-.0330798{col 56}{space 2} .0282883{col 67}{space 1}   -1.17{col 76}{space 3}0.242{col 84}{space 4}-.0885267{col 97}{space 3}  .022367
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0250606{col 56}{space 2} .0294183{col 67}{space 1}    0.85{col 76}{space 3}0.394{col 84}{space 4}-.0326011{col 97}{space 3} .0827224
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2}-.0194327{col 56}{space 2} .0279743{col 67}{space 1}   -0.69{col 76}{space 3}0.487{col 84}{space 4}-.0742641{col 97}{space 3} .0353987
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0312444{col 56}{space 2} .0319673{col 67}{space 1}    0.98{col 76}{space 3}0.328{col 84}{space 4}-.0314137{col 97}{space 3} .0939025
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2} -.076225{col 56}{space 2} .0303265{col 67}{space 1}   -2.51{col 76}{space 3}0.012{col 84}{space 4}-.1356669{col 97}{space 3} -.016783
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}-.0581876{col 56}{space 2} .0316233{col 67}{space 1}   -1.84{col 76}{space 3}0.066{col 84}{space 4}-.1201714{col 97}{space 3} .0037963
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2}-.0429746{col 56}{space 2} .0210494{col 67}{space 1}   -2.04{col 76}{space 3}0.041{col 84}{space 4}-.0842327{col 97}{space 3}-.0017164
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2}-.0109543{col 56}{space 2} .0228849{col 67}{space 1}   -0.48{col 76}{space 3}0.632{col 84}{space 4}-.0558104{col 97}{space 3} .0339017
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2}-.0139683{col 56}{space 2} .0223964{col 67}{space 1}   -0.62{col 76}{space 3}0.533{col 84}{space 4}-.0578668{col 97}{space 3} .0299301
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0932657{col 56}{space 2} .0288464{col 67}{space 1}   -3.23{col 76}{space 3}0.001{col 84}{space 4}-.1498065{col 97}{space 3}-.0367249
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2}-.0261599{col 56}{space 2} .0332579{col 67}{space 1}   -0.79{col 76}{space 3}0.432{col 84}{space 4}-.0913476{col 97}{space 3} .0390278
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0278693{col 56}{space 2} .0413802{col 67}{space 1}    0.67{col 76}{space 3}0.501{col 84}{space 4}-.0532385{col 97}{space 3} .1089772
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} -.007674{col 56}{space 2} .0237007{col 67}{space 1}   -0.32{col 76}{space 3}0.746{col 84}{space 4} -.054129{col 97}{space 3} .0387809
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2}-.0636515{col 56}{space 2} .0381172{col 67}{space 1}   -1.67{col 76}{space 3}0.095{col 84}{space 4}-.1383637{col 97}{space 3} .0110607
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0047384{col 56}{space 2} .0239594{col 67}{space 1}    0.20{col 76}{space 3}0.843{col 84}{space 4}-.0422236{col 97}{space 3} .0517004
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0797461{col 56}{space 2} .0564823{col 67}{space 1}    1.41{col 76}{space 3}0.158{col 84}{space 4} -.030963{col 97}{space 3} .1904551
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2}-.0422805{col 56}{space 2} .0247783{col 67}{space 1}   -1.71{col 76}{space 3}0.088{col 84}{space 4}-.0908477{col 97}{space 3} .0062866
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .1773995{col 56}{space 2} .0901692{col 67}{space 1}    1.97{col 76}{space 3}0.049{col 84}{space 4} .0006619{col 97}{space 3} .3541371
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2} -.099596{col 56}{space 2} .0311278{col 67}{space 1}   -3.20{col 76}{space 3}0.001{col 84}{space 4}-.1606086{col 97}{space 3}-.0385834
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0026417{col 56}{space 2} .0437664{col 67}{space 1}    0.06{col 76}{space 3}0.952{col 84}{space 4}-.0831434{col 97}{space 3} .0884268
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0538541{col 56}{space 2} .0268201{col 67}{space 1}    2.01{col 76}{space 3}0.045{col 84}{space 4} .0012849{col 97}{space 3} .1064232
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2}-.0283877{col 56}{space 2} .0207583{col 67}{space 1}   -1.37{col 76}{space 3}0.171{col 84}{space 4}-.0690753{col 97}{space 3}    .0123
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2}-.0069523{col 56}{space 2} .0268594{col 67}{space 1}   -0.26{col 76}{space 3}0.796{col 84}{space 4}-.0595986{col 97}{space 3}  .045694
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1277686{col 56}{space 2} .0225932{col 67}{space 1}   -5.66{col 76}{space 3}0.000{col 84}{space 4}-.1720528{col 97}{space 3}-.0834844
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0996215{col 56}{space 2} .0453963{col 67}{space 1}    2.19{col 76}{space 3}0.028{col 84}{space 4} .0106417{col 97}{space 3} .1886013
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2}-.0160447{col 56}{space 2} .0251363{col 67}{space 1}   -0.64{col 76}{space 3}0.523{col 84}{space 4}-.0653135{col 97}{space 3} .0332242
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0041753{col 56}{space 2} .0124447{col 67}{space 1}    0.34{col 76}{space 3}0.737{col 84}{space 4}-.0202172{col 97}{space 3} .0285678
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0084113{col 56}{space 2}  .011945{col 67}{space 1}    0.70{col 76}{space 3}0.481{col 84}{space 4}-.0150017{col 97}{space 3} .0318244
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0233214{col 56}{space 2}  .013117{col 67}{space 1}    1.78{col 76}{space 3}0.075{col 84}{space 4}-.0023888{col 97}{space 3} .0490316
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0248466{col 56}{space 2} .0121088{col 67}{space 1}    2.05{col 76}{space 3}0.040{col 84}{space 4} .0011125{col 97}{space 3} .0485807
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0013995{col 56}{space 2}  .016556{col 67}{space 1}    0.08{col 76}{space 3}0.933{col 84}{space 4}-.0310513{col 97}{space 3} .0338504
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}  .044944{col 56}{space 2} .0155444{col 67}{space 1}    2.89{col 76}{space 3}0.004{col 84}{space 4}  .014476{col 97}{space 3} .0754121
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0130542{col 56}{space 2} .0051481{col 67}{space 1}    2.54{col 76}{space 3}0.011{col 84}{space 4} .0029636{col 97}{space 3} .0231447
{txt}{space 39}age {c |}{col 44}{res}{space 2}  .001493{col 56}{space 2} .0002387{col 67}{space 1}    6.26{col 76}{space 3}0.000{col 84}{space 4} .0010252{col 97}{space 3} .0019608
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}  .002915{col 56}{space 2}  .007071{col 67}{space 1}    0.41{col 76}{space 3}0.680{col 84}{space 4}-.0109448{col 97}{space 3} .0167747
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0418259{col 56}{space 2} .0091982{col 67}{space 1}    4.55{col 76}{space 3}0.000{col 84}{space 4} .0237968{col 97}{space 3}  .059855
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0091127{col 56}{space 2} .0103791{col 67}{space 1}    0.88{col 76}{space 3}0.380{col 84}{space 4} -.011231{col 97}{space 3} .0294563
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0059716{col 56}{space 2} .0136765{col 67}{space 1}    0.44{col 76}{space 3}0.662{col 84}{space 4}-.0208353{col 97}{space 3} .0327784
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1965333{col 56}{space 2}  .045435{col 67}{space 1}    4.33{col 76}{space 3}0.000{col 84}{space 4} .1074776{col 97}{space 3}  .285589
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}23174{txt}) = {res}4.399{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.2476
{col 25}{txt}Prob>|t| = {res}    0.8268

95%{txt} confidence set for null hypothesis expression: {res}[−.04329, .03162]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:23,270}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .1825526{col 28}{space 2} .0025324{col 39}{space 5} .1775889{col 53}{space 3} .1875163
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#0b.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#1.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:60,092}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:56}
{txt}{col 53}{lalign 17:F({res:159}, {res:59877})}{col 70} = {res}{ralign 6:10.88}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0504}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0470}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4042}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2} .0035374{col 58}{space 2} .0060303{col 69}{space 1}    0.59{col 78}{space 3}0.557{col 86}{space 4} -.008282{col 99}{space 3} .0153568
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2}   .33441{col 58}{space 2} .2837274{col 69}{space 1}    1.18{col 78}{space 3}0.239{col 86}{space 4}-.2216968{col 99}{space 3} .8905168
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2} .2329959{col 58}{space 2}   .38049{col 69}{space 1}    0.61{col 78}{space 3}0.540{col 86}{space 4}-.5127658{col 99}{space 3} .9787577
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.2234546{col 58}{space 2} .3240879{col 69}{space 1}   -0.69{col 78}{space 3}0.491{col 86}{space 4} -.858668{col 99}{space 3} .4117587
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.4971305{col 58}{space 2} .3246127{col 69}{space 1}   -1.53{col 78}{space 3}0.126{col 86}{space 4}-1.133373{col 99}{space 3} .1391116
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}-.6289697{col 58}{space 2} .3955809{col 69}{space 1}   -1.59{col 78}{space 3}0.112{col 86}{space 4} -1.40431{col 99}{space 3} .1463702
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}-.5907138{col 58}{space 2} .3280703{col 69}{space 1}   -1.80{col 78}{space 3}0.072{col 86}{space 4}-1.233733{col 99}{space 3} .0523051
{txt}{space 39}1993  {c |}{col 46}{res}{space 2}-.3247739{col 58}{space 2} .3134805{col 69}{space 1}   -1.04{col 78}{space 3}0.300{col 86}{space 4}-.9391969{col 99}{space 3} .2896491
{txt}{space 39}1994  {c |}{col 46}{res}{space 2}-.1353224{col 58}{space 2} .3138894{col 69}{space 1}   -0.43{col 78}{space 3}0.666{col 86}{space 4}-.7505467{col 99}{space 3}  .479902
{txt}{space 39}1995  {c |}{col 46}{res}{space 2}-.1616086{col 58}{space 2} .3108397{col 69}{space 1}   -0.52{col 78}{space 3}0.603{col 86}{space 4}-.7708555{col 99}{space 3} .4476383
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}-.2568744{col 58}{space 2} .3243525{col 69}{space 1}   -0.79{col 78}{space 3}0.428{col 86}{space 4}-.8926065{col 99}{space 3} .3788577
{txt}{space 39}1997  {c |}{col 46}{res}{space 2}  .029511{col 58}{space 2} .3613502{col 69}{space 1}    0.08{col 78}{space 3}0.935{col 86}{space 4}-.6787366{col 99}{space 3} .7377587
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .3584727{col 58}{space 2}  .376127{col 69}{space 1}    0.95{col 78}{space 3}0.341{col 86}{space 4}-.3787376{col 99}{space 3} 1.095683
{txt}{space 39}1999  {c |}{col 46}{res}{space 2}-.2679242{col 58}{space 2} .3675505{col 69}{space 1}   -0.73{col 78}{space 3}0.466{col 86}{space 4}-.9883245{col 99}{space 3}  .452476
{txt}{space 39}2000  {c |}{col 46}{res}{space 2} .0203109{col 58}{space 2} .2864773{col 69}{space 1}    0.07{col 78}{space 3}0.943{col 86}{space 4}-.5411857{col 99}{space 3} .5818075
{txt}{space 39}2001  {c |}{col 46}{res}{space 2}-.4226573{col 58}{space 2} .3419825{col 69}{space 1}   -1.24{col 78}{space 3}0.216{col 86}{space 4}-1.092944{col 99}{space 3} .2476297
{txt}{space 39}2002  {c |}{col 46}{res}{space 2} .1291716{col 58}{space 2} .3507324{col 69}{space 1}    0.37{col 78}{space 3}0.713{col 86}{space 4}-.5582652{col 99}{space 3} .8166084
{txt}{space 39}2003  {c |}{col 46}{res}{space 2}-.5868344{col 58}{space 2} .3458711{col 69}{space 1}   -1.70{col 78}{space 3}0.090{col 86}{space 4}-1.264743{col 99}{space 3} .0910741
{txt}{space 39}2004  {c |}{col 46}{res}{space 2}-.0748281{col 58}{space 2} .2850304{col 69}{space 1}   -0.26{col 78}{space 3}0.793{col 86}{space 4}-.6334888{col 99}{space 3} .4838326
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}-.8305344{col 58}{space 2} .3480598{col 69}{space 1}   -2.39{col 78}{space 3}0.017{col 86}{space 4}-1.512733{col 99}{space 3} -.148336
{txt}{space 39}2006  {c |}{col 46}{res}{space 2}-.6920725{col 58}{space 2}  .326884{col 69}{space 1}   -2.12{col 78}{space 3}0.034{col 86}{space 4}-1.332766{col 99}{space 3}-.0513788
{txt}{space 39}2007  {c |}{col 46}{res}{space 2}-.4538661{col 58}{space 2} .3126105{col 69}{space 1}   -1.45{col 78}{space 3}0.147{col 86}{space 4}-1.066584{col 99}{space 3} .1588517
{txt}{space 39}2008  {c |}{col 46}{res}{space 2}-.2767556{col 58}{space 2} .2835252{col 69}{space 1}   -0.98{col 78}{space 3}0.329{col 86}{space 4} -.832466{col 99}{space 3} .2789547
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.7276518{col 58}{space 2} .3174271{col 69}{space 1}   -2.29{col 78}{space 3}0.022{col 86}{space 4} -1.34981{col 99}{space 3}-.1054935
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}-.6177936{col 58}{space 2} .3507298{col 69}{space 1}   -1.76{col 78}{space 3}0.078{col 86}{space 4}-1.305225{col 99}{space 3}  .069638
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.4604506{col 58}{space 2} .3813036{col 69}{space 1}   -1.21{col 78}{space 3}0.227{col 86}{space 4}-1.207807{col 99}{space 3} .2869059
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .0946337{col 58}{space 2} .3842748{col 69}{space 1}    0.25{col 78}{space 3}0.805{col 86}{space 4}-.6585463{col 99}{space 3} .8478136
{txt}{space 39}2013  {c |}{col 46}{res}{space 2}-.2497657{col 58}{space 2}  .373406{col 69}{space 1}   -0.67{col 78}{space 3}0.504{col 86}{space 4}-.9816427{col 99}{space 3} .4821114
{txt}{space 39}2014  {c |}{col 46}{res}{space 2}-.4533346{col 58}{space 2} .3870839{col 69}{space 1}   -1.17{col 78}{space 3}0.242{col 86}{space 4} -1.21202{col 99}{space 3} .3053512
{txt}{space 39}2015  {c |}{col 46}{res}{space 2}-.3826761{col 58}{space 2} .3762935{col 69}{space 1}   -1.02{col 78}{space 3}0.309{col 86}{space 4}-1.120213{col 99}{space 3} .3548605
{txt}{space 39}2016  {c |}{col 46}{res}{space 2}-.3401118{col 58}{space 2} .3658466{col 69}{space 1}   -0.93{col 78}{space 3}0.353{col 86}{space 4}-1.057173{col 99}{space 3} .3769489
{txt}{space 39}2017  {c |}{col 46}{res}{space 2}-.2642456{col 58}{space 2} .3648197{col 69}{space 1}   -0.72{col 78}{space 3}0.469{col 86}{space 4}-.9792936{col 99}{space 3} .4508024
{txt}{space 39}2018  {c |}{col 46}{res}{space 2}-.3924398{col 58}{space 2} .3662815{col 69}{space 1}   -1.07{col 78}{space 3}0.284{col 86}{space 4}-1.110353{col 99}{space 3} .3254733
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.6701484{col 58}{space 2} .3279144{col 69}{space 1}   -2.04{col 78}{space 3}0.041{col 86}{space 4}-1.312862{col 99}{space 3} -.027435
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}-.5997505{col 58}{space 2} .3326222{col 69}{space 1}   -1.80{col 78}{space 3}0.071{col 86}{space 4}-1.251691{col 99}{space 3} .0521902
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.7411045{col 58}{space 2} .3474554{col 69}{space 1}   -2.13{col 78}{space 3}0.033{col 86}{space 4}-1.422118{col 99}{space 3}-.0600906
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.7994005{col 58}{space 2} .3187252{col 69}{space 1}   -2.51{col 78}{space 3}0.012{col 86}{space 4}-1.424103{col 99}{space 3} -.174698
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.0957162{col 58}{space 2} .0379563{col 69}{space 1}   -2.52{col 78}{space 3}0.012{col 86}{space 4}-.1701107{col 99}{space 3}-.0213217
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2}-.0887981{col 58}{space 2} .0343569{col 69}{space 1}   -2.58{col 78}{space 3}0.010{col 86}{space 4}-.1561377{col 99}{space 3}-.0214585
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} -.001889{col 58}{space 2} .0240491{col 69}{space 1}   -0.08{col 78}{space 3}0.937{col 86}{space 4}-.0490253{col 99}{space 3} .0452473
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2}-.0281159{col 58}{space 2} .0291096{col 69}{space 1}   -0.97{col 78}{space 3}0.334{col 86}{space 4}-.0851708{col 99}{space 3} .0289391
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2} -.002174{col 58}{space 2} .0286428{col 69}{space 1}   -0.08{col 78}{space 3}0.939{col 86}{space 4}-.0583141{col 99}{space 3} .0539661
{txt}{space 38}avila  {c |}{col 46}{res}{space 2}-.0519822{col 58}{space 2} .0455362{col 69}{space 1}   -1.14{col 78}{space 3}0.254{col 86}{space 4}-.1412333{col 99}{space 3} .0372689
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2}-.0169536{col 58}{space 2}  .028464{col 69}{space 1}   -0.60{col 78}{space 3}0.551{col 86}{space 4}-.0727431{col 99}{space 3} .0388359
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.1105109{col 58}{space 2} .0258178{col 69}{space 1}   -4.28{col 78}{space 3}0.000{col 86}{space 4}-.1611139{col 99}{space 3}-.0599079
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0166398{col 58}{space 2} .0380579{col 69}{space 1}    0.44{col 78}{space 3}0.662{col 86}{space 4}-.0579538{col 99}{space 3} .0912334
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2} .0292253{col 58}{space 2} .0378196{col 69}{space 1}    0.77{col 78}{space 3}0.440{col 86}{space 4}-.0449011{col 99}{space 3} .1033518
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2}-.0456004{col 58}{space 2} .0261756{col 69}{space 1}   -1.74{col 78}{space 3}0.081{col 86}{space 4}-.0969047{col 99}{space 3} .0057039
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2}-.0448979{col 58}{space 2} .0260269{col 69}{space 1}   -1.73{col 78}{space 3}0.085{col 86}{space 4}-.0959108{col 99}{space 3}  .006115
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2}-.0306908{col 58}{space 2}  .028944{col 69}{space 1}   -1.06{col 78}{space 3}0.289{col 86}{space 4}-.0874211{col 99}{space 3} .0260395
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2}-.0450208{col 58}{space 2} .0367141{col 69}{space 1}   -1.23{col 78}{space 3}0.220{col 86}{space 4}-.1169806{col 99}{space 3}  .026939
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2}-.0502301{col 58}{space 2} .0272996{col 69}{space 1}   -1.84{col 78}{space 3}0.066{col 86}{space 4}-.1037374{col 99}{space 3} .0032772
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2} .0110446{col 58}{space 2} .0490455{col 69}{space 1}    0.23{col 78}{space 3}0.822{col 86}{space 4}-.0850847{col 99}{space 3}  .107174
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.1686141{col 58}{space 2} .0295844{col 69}{space 1}   -5.70{col 78}{space 3}0.000{col 86}{space 4}-.2265996{col 99}{space 3}-.1106287
{txt}{space 37}girona  {c |}{col 46}{res}{space 2}-.1482101{col 58}{space 2} .0339026{col 69}{space 1}   -4.37{col 78}{space 3}0.000{col 86}{space 4}-.2146593{col 99}{space 3} -.081761
{txt}{space 36}granada  {c |}{col 46}{res}{space 2}-.0050841{col 58}{space 2} .0274004{col 69}{space 1}   -0.19{col 78}{space 3}0.853{col 86}{space 4} -.058789{col 99}{space 3} .0486208
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .0001872{col 58}{space 2} .0339133{col 69}{space 1}    0.01{col 78}{space 3}0.996{col 86}{space 4}-.0662829{col 99}{space 3} .0666573
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2}-.0312673{col 58}{space 2} .0363915{col 69}{space 1}   -0.86{col 78}{space 3}0.390{col 86}{space 4}-.1025948{col 99}{space 3} .0400602
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2}-.0333301{col 58}{space 2} .0394813{col 69}{space 1}   -0.84{col 78}{space 3}0.399{col 86}{space 4}-.1107137{col 99}{space 3} .0440535
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2}-.0399279{col 58}{space 2} .0328691{col 69}{space 1}   -1.21{col 78}{space 3}0.224{col 86}{space 4}-.1043515{col 99}{space 3} .0244957
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2}-.0330871{col 58}{space 2} .0300516{col 69}{space 1}   -1.10{col 78}{space 3}0.271{col 86}{space 4}-.0919883{col 99}{space 3}  .025814
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0248978{col 58}{space 2} .0312515{col 69}{space 1}    0.80{col 78}{space 3}0.426{col 86}{space 4}-.0363553{col 99}{space 3} .0861508
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} -.020227{col 58}{space 2} .0297051{col 69}{space 1}   -0.68{col 78}{space 3}0.496{col 86}{space 4} -.078449{col 99}{space 3} .0379951
{txt}{space 39}leon  {c |}{col 46}{res}{space 2} .0311507{col 58}{space 2} .0339598{col 69}{space 1}    0.92{col 78}{space 3}0.359{col 86}{space 4}-.0354107{col 99}{space 3} .0977121
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2} -.076563{col 58}{space 2} .0322147{col 69}{space 1}   -2.38{col 78}{space 3}0.017{col 86}{space 4}-.1397039{col 99}{space 3}-.0134221
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2}-.0606277{col 58}{space 2} .0334865{col 69}{space 1}   -1.81{col 78}{space 3}0.070{col 86}{space 4}-.1262613{col 99}{space 3} .0050058
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2}  -.04342{col 58}{space 2} .0223561{col 69}{space 1}   -1.94{col 78}{space 3}0.052{col 86}{space 4} -.087238{col 99}{space 3} .0003979
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2}-.0109603{col 58}{space 2} .0243115{col 69}{space 1}   -0.45{col 78}{space 3}0.652{col 86}{space 4}-.0586108{col 99}{space 3} .0366903
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2}-.0139371{col 58}{space 2} .0237924{col 69}{space 1}   -0.59{col 78}{space 3}0.558{col 86}{space 4}-.0605704{col 99}{space 3} .0326962
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0931811{col 58}{space 2} .0306443{col 69}{space 1}   -3.04{col 78}{space 3}0.002{col 86}{space 4}-.1532441{col 99}{space 3}-.0331181
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} -.025994{col 58}{space 2} .0353305{col 69}{space 1}   -0.74{col 78}{space 3}0.462{col 86}{space 4} -.095242{col 99}{space 3} .0432539
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2}  .028067{col 58}{space 2}  .043959{col 69}{space 1}    0.64{col 78}{space 3}0.523{col 86}{space 4}-.0580929{col 99}{space 3} .1142268
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2}-.0075586{col 58}{space 2} .0251777{col 69}{space 1}   -0.30{col 78}{space 3}0.764{col 86}{space 4} -.056907{col 99}{space 3} .0417899
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2}-.0646229{col 58}{space 2}  .040479{col 69}{space 1}   -1.60{col 78}{space 3}0.110{col 86}{space 4}-.1439618{col 99}{space 3}  .014716
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .0045145{col 58}{space 2} .0254517{col 69}{space 1}    0.18{col 78}{space 3}0.859{col 86}{space 4}-.0453709{col 99}{space 3} .0543999
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2}  .080127{col 58}{space 2} .0600016{col 69}{space 1}    1.34{col 78}{space 3}0.182{col 86}{space 4}-.0374763{col 99}{space 3} .1977302
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2}-.0421177{col 58}{space 2} .0263223{col 69}{space 1}   -1.60{col 78}{space 3}0.110{col 86}{space 4}-.0937095{col 99}{space 3}  .009474
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .1776265{col 58}{space 2} .0957894{col 69}{space 1}    1.85{col 78}{space 3}0.064{col 86}{space 4}-.0101211{col 99}{space 3} .3653742
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2}-.1015544{col 58}{space 2} .0329975{col 69}{space 1}   -3.08{col 78}{space 3}0.002{col 86}{space 4}-.1662295{col 99}{space 3}-.0368792
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2} .0028483{col 58}{space 2}  .046494{col 69}{space 1}    0.06{col 78}{space 3}0.951{col 86}{space 4}-.0882801{col 99}{space 3} .0939768
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2} .0537984{col 58}{space 2} .0284918{col 69}{space 1}    1.89{col 78}{space 3}0.059{col 86}{space 4}-.0020457{col 99}{space 3} .1096425
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2}-.0286438{col 58}{space 2} .0220504{col 69}{space 1}   -1.30{col 78}{space 3}0.194{col 86}{space 4}-.0718627{col 99}{space 3} .0145751
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2}-.0068257{col 58}{space 2} .0285334{col 69}{space 1}   -0.24{col 78}{space 3}0.811{col 86}{space 4}-.0627511{col 99}{space 3} .0490998
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.1296445{col 58}{space 2} .0239121{col 69}{space 1}   -5.42{col 78}{space 3}0.000{col 86}{space 4}-.1765123{col 99}{space 3}-.0827767
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2} .0984877{col 58}{space 2} .0482098{col 69}{space 1}    2.04{col 78}{space 3}0.041{col 86}{space 4} .0039963{col 99}{space 3} .1929791
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2}-.0159949{col 58}{space 2} .0267031{col 69}{space 1}   -0.60{col 78}{space 3}0.549{col 86}{space 4}-.0683331{col 99}{space 3} .0363432
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0040579{col 58}{space 2} .0132198{col 69}{space 1}    0.31{col 78}{space 3}0.759{col 86}{space 4} -.021853{col 99}{space 3} .0299689
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0082464{col 58}{space 2} .0126883{col 69}{space 1}    0.65{col 78}{space 3}0.516{col 86}{space 4}-.0166227{col 99}{space 3} .0331155
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0231415{col 58}{space 2} .0139332{col 69}{space 1}    1.66{col 78}{space 3}0.097{col 86}{space 4}-.0041676{col 99}{space 3} .0504507
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2} .0247142{col 58}{space 2} .0128628{col 69}{space 1}    1.92{col 78}{space 3}0.055{col 86}{space 4}-.0004969{col 99}{space 3} .0499253
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2} .0012466{col 58}{space 2} .0175872{col 69}{space 1}    0.07{col 78}{space 3}0.943{col 86}{space 4}-.0332244{col 99}{space 3} .0357176
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2} .0447416{col 58}{space 2} .0165118{col 69}{space 1}    2.71{col 78}{space 3}0.007{col 86}{space 4} .0123784{col 99}{space 3} .0771049
{txt}{space 44} {c |}
{space 38}female {c |}{col 46}{res}{space 2} .0021719{col 58}{space 2} .0045641{col 69}{space 1}    0.48{col 78}{space 3}0.634{col 86}{space 4}-.0067738{col 99}{space 3} .0111176
{txt}{space 41}age {c |}{col 46}{res}{space 2} .0014935{col 58}{space 2} .0002535{col 69}{space 1}    5.89{col 78}{space 3}0.000{col 86}{space 4} .0009966{col 99}{space 3} .0019904
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0029666{col 58}{space 2} .0075116{col 69}{space 1}    0.39{col 78}{space 3}0.693{col 86}{space 4}-.0117561{col 99}{space 3} .0176894
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0418165{col 58}{space 2} .0097716{col 69}{space 1}    4.28{col 78}{space 3}0.000{col 86}{space 4} .0226641{col 99}{space 3} .0609688
{txt}{space 36}Student  {c |}{col 46}{res}{space 2} .0090976{col 58}{space 2}  .011026{col 69}{space 1}    0.83{col 78}{space 3}0.409{col 86}{space 4}-.0125135{col 99}{space 3} .0307086
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2} .0059779{col 58}{space 2}  .014529{col 69}{space 1}    0.41{col 78}{space 3}0.681{col 86}{space 4} -.022499{col 99}{space 3} .0344549
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2} .0729152{col 58}{space 2} .0209621{col 69}{space 1}    3.48{col 78}{space 3}0.001{col 86}{space 4} .0318294{col 99}{space 3}  .114001
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2} .0584977{col 58}{space 2} .0177577{col 69}{space 1}    3.29{col 78}{space 3}0.001{col 86}{space 4} .0236926{col 99}{space 3} .0933028
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2} .0494101{col 58}{space 2} .0165478{col 69}{space 1}    2.99{col 78}{space 3}0.003{col 86}{space 4} .0169763{col 99}{space 3} .0818438
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0206165{col 58}{space 2} .0178614{col 69}{space 1}    1.15{col 78}{space 3}0.248{col 86}{space 4} -.014392{col 99}{space 3}  .055625
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2}  .045771{col 58}{space 2} .0162282{col 69}{space 1}    2.82{col 78}{space 3}0.005{col 86}{space 4} .0139637{col 99}{space 3} .0775783
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}  .015606{col 58}{space 2} .0238815{col 69}{space 1}    0.65{col 78}{space 3}0.513{col 86}{space 4}-.0312018{col 99}{space 3} .0624138
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#female {c |}
{space 40}0 1  {c |}{col 46}{res}{space 2} .0108161{col 58}{space 2} .0071227{col 69}{space 1}    1.52{col 78}{space 3}0.129{col 86}{space 4}-.0031444{col 99}{space 3} .0247766
{txt}{space 40}1 0  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 40}1 1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2} .0000149{col 58}{space 2} .0003331{col 69}{space 1}    0.04{col 78}{space 3}0.964{col 86}{space 4} -.000638{col 99}{space 3} .0006678
{txt}{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2} .0020737{col 58}{space 2} .0167071{col 69}{space 1}    0.12{col 78}{space 3}0.901{col 86}{space 4}-.0306722{col 99}{space 3} .0348196
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2}-.0194497{col 58}{space 2} .0182629{col 69}{space 1}   -1.06{col 78}{space 3}0.287{col 86}{space 4} -.055245{col 99}{space 3} .0163456
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2}-.0227387{col 58}{space 2}  .020008{col 69}{space 1}   -1.14{col 78}{space 3}0.256{col 86}{space 4}-.0619546{col 99}{space 3} .0164771
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2}-.0212068{col 58}{space 2} .0209547{col 69}{space 1}   -1.01{col 78}{space 3}0.312{col 86}{space 4}-.0622781{col 99}{space 3} .0198646
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2} .0015968{col 58}{space 2}  .034641{col 69}{space 1}    0.05{col 78}{space 3}0.963{col 86}{space 4}-.0662996{col 99}{space 3} .0694933
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2}-.0304444{col 58}{space 2} .0461744{col 69}{space 1}   -0.66{col 78}{space 3}0.510{col 86}{space 4}-.1209464{col 99}{space 3} .0600576
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2} .1195275{col 58}{space 2} .0459418{col 69}{space 1}    2.60{col 78}{space 3}0.009{col 86}{space 4} .0294813{col 99}{space 3} .2095736
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0838508{col 58}{space 2} .0354778{col 69}{space 1}    2.36{col 78}{space 3}0.018{col 86}{space 4} .0143141{col 99}{space 3} .1533875
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0951417{col 58}{space 2} .0413332{col 69}{space 1}    2.30{col 78}{space 3}0.021{col 86}{space 4} .0141285{col 99}{space 3} .1761548
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2} .0261537{col 58}{space 2} .0389526{col 69}{space 1}    0.67{col 78}{space 3}0.502{col 86}{space 4}-.0501936{col 99}{space 3}  .102501
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2} .0563129{col 58}{space 2} .0578293{col 69}{space 1}    0.97{col 78}{space 3}0.330{col 86}{space 4}-.0570327{col 99}{space 3} .1696586
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .0869763{col 58}{space 2} .0403884{col 69}{space 1}    2.15{col 78}{space 3}0.031{col 86}{space 4}  .007815{col 99}{space 3} .1661377
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} .0670673{col 58}{space 2} .0335303{col 69}{space 1}    2.00{col 78}{space 3}0.045{col 86}{space 4} .0013479{col 99}{space 3} .1327868
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0288981{col 58}{space 2} .0483019{col 69}{space 1}   -0.60{col 78}{space 3}0.550{col 86}{space 4}  -.12357{col 99}{space 3} .0657738
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2} .0366359{col 58}{space 2} .0483853{col 69}{space 1}    0.76{col 78}{space 3}0.449{col 86}{space 4}-.0581995{col 99}{space 3} .1314713
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .1147727{col 58}{space 2}  .037882{col 69}{space 1}    3.03{col 78}{space 3}0.002{col 86}{space 4} .0405239{col 99}{space 3} .1890215
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2}   .11504{col 58}{space 2} .0375955{col 69}{space 1}    3.06{col 78}{space 3}0.002{col 86}{space 4} .0413526{col 99}{space 3} .1887274
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .1194123{col 58}{space 2} .0403736{col 69}{space 1}    2.96{col 78}{space 3}0.003{col 86}{space 4}   .04028{col 99}{space 3} .1985446
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2}  .108205{col 58}{space 2} .0477265{col 69}{space 1}    2.27{col 78}{space 3}0.023{col 86}{space 4} .0146608{col 99}{space 3} .2017491
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2} .0225512{col 58}{space 2} .0382219{col 69}{space 1}    0.59{col 78}{space 3}0.555{col 86}{space 4}-.0523639{col 99}{space 3} .0974662
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2}-.0016313{col 58}{space 2} .0601763{col 69}{space 1}   -0.03{col 78}{space 3}0.978{col 86}{space 4} -.119577{col 99}{space 3} .1163144
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0175395{col 58}{space 2} .0381932{col 69}{space 1}   -0.46{col 78}{space 3}0.646{col 86}{space 4}-.0923983{col 99}{space 3} .0573194
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2} .0672325{col 58}{space 2} .0426694{col 69}{space 1}    1.58{col 78}{space 3}0.115{col 86}{space 4}-.0163996{col 99}{space 3} .1508646
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2} .0168558{col 58}{space 2} .0389013{col 69}{space 1}    0.43{col 78}{space 3}0.665{col 86}{space 4}-.0593909{col 99}{space 3} .0931025
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2}-.0086901{col 58}{space 2} .0461734{col 69}{space 1}   -0.19{col 78}{space 3}0.851{col 86}{space 4}-.0991901{col 99}{space 3}   .08181
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2} .1001678{col 58}{space 2} .0479272{col 69}{space 1}    2.09{col 78}{space 3}0.037{col 86}{space 4} .0062304{col 99}{space 3} .1941053
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2} .0235757{col 58}{space 2} .0504037{col 69}{space 1}    0.47{col 78}{space 3}0.640{col 86}{space 4}-.0752158{col 99}{space 3} .1223671
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .1032713{col 58}{space 2} .0405648{col 69}{space 1}    2.55{col 78}{space 3}0.011{col 86}{space 4} .0237642{col 99}{space 3} .1827784
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2}  .050643{col 58}{space 2} .0412001{col 69}{space 1}    1.23{col 78}{space 3}0.219{col 86}{space 4}-.0301093{col 99}{space 3} .1313952
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0674649{col 58}{space 2} .0430268{col 69}{space 1}   -1.57{col 78}{space 3}0.117{col 86}{space 4}-.1517975{col 99}{space 3} .0168678
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2} .0940369{col 58}{space 2} .0398476{col 69}{space 1}    2.36{col 78}{space 3}0.018{col 86}{space 4} .0159356{col 99}{space 3} .1721383
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2}-.0516007{col 58}{space 2} .0445445{col 69}{space 1}   -1.16{col 78}{space 3}0.247{col 86}{space 4}-.1389081{col 99}{space 3} .0357067
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2}-.0540785{col 58}{space 2} .0451206{col 69}{space 1}   -1.20{col 78}{space 3}0.231{col 86}{space 4} -.142515{col 99}{space 3}  .034358
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0522386{col 58}{space 2} .0445183{col 69}{space 1}    1.17{col 78}{space 3}0.241{col 86}{space 4}-.0350174{col 99}{space 3} .1394946
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0441567{col 58}{space 2} .0314936{col 69}{space 1}    1.40{col 78}{space 3}0.161{col 86}{space 4}-.0175708{col 99}{space 3} .1058842
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2} .1001671{col 58}{space 2} .0320239{col 69}{space 1}    3.13{col 78}{space 3}0.002{col 86}{space 4} .0374001{col 99}{space 3} .1629341
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2} .0542409{col 58}{space 2}  .033549{col 69}{space 1}    1.62{col 78}{space 3}0.106{col 86}{space 4}-.0115153{col 99}{space 3}  .119997
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2} .0148513{col 58}{space 2} .0405787{col 69}{space 1}    0.37{col 78}{space 3}0.714{col 86}{space 4}-.0646831{col 99}{space 3} .0943858
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2} .0226747{col 58}{space 2} .0451926{col 69}{space 1}    0.50{col 78}{space 3}0.616{col 86}{space 4}-.0659029{col 99}{space 3} .1112523
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2} .0338717{col 58}{space 2} .0555025{col 69}{space 1}    0.61{col 78}{space 3}0.542{col 86}{space 4}-.0749133{col 99}{space 3} .1426567
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}-.0142608{col 58}{space 2} .0357782{col 69}{space 1}   -0.40{col 78}{space 3}0.690{col 86}{space 4}-.0843862{col 99}{space 3} .0558646
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .1244627{col 58}{space 2}  .051064{col 69}{space 1}    2.44{col 78}{space 3}0.015{col 86}{space 4} .0243771{col 99}{space 3} .2245483
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2} .0430723{col 58}{space 2} .0363932{col 69}{space 1}    1.18{col 78}{space 3}0.237{col 86}{space 4}-.0282585{col 99}{space 3} .1144031
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.1058339{col 58}{space 2} .0697783{col 69}{space 1}   -1.52{col 78}{space 3}0.129{col 86}{space 4}-.2425997{col 99}{space 3} .0309318
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .1028135{col 58}{space 2} .0333865{col 69}{space 1}    3.08{col 78}{space 3}0.002{col 86}{space 4} .0373759{col 99}{space 3} .1682511
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2}-.1383385{col 58}{space 2}   .10462{col 69}{space 1}   -1.32{col 78}{space 3}0.186{col 86}{space 4}-.3433941{col 99}{space 3} .0667172
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2}  .079385{col 58}{space 2} .0425445{col 69}{space 1}    1.87{col 78}{space 3}0.062{col 86}{space 4}-.0040024{col 99}{space 3} .1627725
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2} .0130446{col 58}{space 2} .0563118{col 69}{space 1}    0.23{col 78}{space 3}0.817{col 86}{space 4}-.0973267{col 99}{space 3} .1234159
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2}-.0168168{col 58}{space 2} .0404883{col 69}{space 1}   -0.42{col 78}{space 3}0.678{col 86}{space 4}-.0961741{col 99}{space 3} .0625405
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2} .0487649{col 58}{space 2} .0298904{col 69}{space 1}    1.63{col 78}{space 3}0.103{col 86}{space 4}-.0098204{col 99}{space 3} .1073502
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2} .0259568{col 58}{space 2} .0400508{col 69}{space 1}    0.65{col 78}{space 3}0.517{col 86}{space 4} -.052543{col 99}{space 3} .1044566
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2} -.059904{col 58}{space 2} .0336805{col 69}{space 1}   -1.78{col 78}{space 3}0.075{col 86}{space 4}-.1259179{col 99}{space 3} .0061099
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2}-.0394975{col 58}{space 2} .0592468{col 69}{space 1}   -0.67{col 78}{space 3}0.505{col 86}{space 4}-.1556215{col 99}{space 3} .0766265
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1355132{col 58}{space 2} .0379097{col 69}{space 1}    3.57{col 78}{space 3}0.000{col 86}{space 4} .0612101{col 99}{space 3} .2098163
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}59877{txt}) = {res}4.385{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.3425
{col 25}{txt}Prob>|t| = {res}    0.7367

95%{txt} confidence set for null hypothesis expression: {res}[−.02064, .02712]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:60,092}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2196465{col 28}{space 2} .0016889{col 39}{space 5} .2163363{col 53}{space 3} .2229568
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_24_25_26_secondary.tex"'})
(1,067,279 observations deleted)
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:20,260}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:35}
{txt}{col 53}{lalign 17:F({res:85}, {res:20140})}{col 70} = {res}{ralign 6:9.14}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0639}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0584}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.4167}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0277073{col 56}{space 2} .0126719{col 67}{space 1}    2.19{col 76}{space 3}0.029{col 84}{space 4} .0028694{col 97}{space 3} .0525452
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .1633716{col 56}{space 2} .3246041{col 67}{space 1}    0.50{col 76}{space 3}0.615{col 84}{space 4}-.4728791{col 97}{space 3} .7996222
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1989  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1990  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1991  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1992  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1993  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1994  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1995  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1996  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1997  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1998  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}1999  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2000  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2001  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2002  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2003  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2004  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2005  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2006  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2007  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2008  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2009  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}1988  {c |}{col 44}{res}{space 2}-.3795736{col 56}{space 2} .5041603{col 67}{space 1}   -0.75{col 76}{space 3}0.452{col 84}{space 4}-1.367769{col 97}{space 3} .6086219
{txt}{space 37}1989  {c |}{col 44}{res}{space 2} -.420286{col 56}{space 2}  .399555{col 67}{space 1}   -1.05{col 76}{space 3}0.293{col 84}{space 4}-1.203447{col 97}{space 3} .3628745
{txt}{space 37}1990  {c |}{col 44}{res}{space 2}  -.55132{col 56}{space 2} .3759939{col 67}{space 1}   -1.47{col 76}{space 3}0.143{col 84}{space 4}-1.288299{col 97}{space 3} .1856589
{txt}{space 37}1991  {c |}{col 44}{res}{space 2}-.7480982{col 56}{space 2} .5056092{col 67}{space 1}   -1.48{col 76}{space 3}0.139{col 84}{space 4}-1.739134{col 97}{space 3} .2429373
{txt}{space 37}1992  {c |}{col 44}{res}{space 2}-.7793268{col 56}{space 2} .4227194{col 67}{space 1}   -1.84{col 76}{space 3}0.065{col 84}{space 4}-1.607891{col 97}{space 3} .0492378
{txt}{space 37}1993  {c |}{col 44}{res}{space 2}-.3879624{col 56}{space 2} .3840831{col 67}{space 1}   -1.01{col 76}{space 3}0.312{col 84}{space 4}-1.140797{col 97}{space 3}  .364872
{txt}{space 37}1994  {c |}{col 44}{res}{space 2}-.4550802{col 56}{space 2} .3833845{col 67}{space 1}   -1.19{col 76}{space 3}0.235{col 84}{space 4}-1.206545{col 97}{space 3} .2963848
{txt}{space 37}1995  {c |}{col 44}{res}{space 2}-.5304454{col 56}{space 2} .3733604{col 67}{space 1}   -1.42{col 76}{space 3}0.155{col 84}{space 4}-1.262262{col 97}{space 3} .2013715
{txt}{space 37}1996  {c |}{col 44}{res}{space 2}-.4681556{col 56}{space 2} .4088488{col 67}{space 1}   -1.15{col 76}{space 3}0.252{col 84}{space 4}-1.269533{col 97}{space 3} .3332215
{txt}{space 37}1997  {c |}{col 44}{res}{space 2}-.2458926{col 56}{space 2} .4627949{col 67}{space 1}   -0.53{col 76}{space 3}0.595{col 84}{space 4}-1.153008{col 97}{space 3} .6612233
{txt}{space 37}1998  {c |}{col 44}{res}{space 2} .3798293{col 56}{space 2} .4842971{col 67}{space 1}    0.78{col 76}{space 3}0.433{col 84}{space 4}-.5694327{col 97}{space 3} 1.329091
{txt}{space 37}1999  {c |}{col 44}{res}{space 2} .5329386{col 56}{space 2} .4750956{col 67}{space 1}    1.12{col 76}{space 3}0.262{col 84}{space 4}-.3982877{col 97}{space 3} 1.464165
{txt}{space 37}2000  {c |}{col 44}{res}{space 2}-.0097906{col 56}{space 2} .3307419{col 67}{space 1}   -0.03{col 76}{space 3}0.976{col 84}{space 4}-.6580718{col 97}{space 3} .6384906
{txt}{space 37}2001  {c |}{col 44}{res}{space 2} -.267803{col 56}{space 2} .4578536{col 67}{space 1}   -0.58{col 76}{space 3}0.559{col 84}{space 4}-1.165234{col 97}{space 3} .6296275
{txt}{space 37}2002  {c |}{col 44}{res}{space 2} -.603269{col 56}{space 2} .4672275{col 67}{space 1}   -1.29{col 76}{space 3}0.197{col 84}{space 4}-1.519073{col 97}{space 3} .3125352
{txt}{space 37}2003  {c |}{col 44}{res}{space 2}-.6375023{col 56}{space 2} .4300997{col 67}{space 1}   -1.48{col 76}{space 3}0.138{col 84}{space 4}-1.480533{col 97}{space 3} .2055282
{txt}{space 37}2004  {c |}{col 44}{res}{space 2} -.142071{col 56}{space 2} .3280814{col 67}{space 1}   -0.43{col 76}{space 3}0.665{col 84}{space 4}-.7851373{col 97}{space 3} .5009953
{txt}{space 37}2005  {c |}{col 44}{res}{space 2}-.0908122{col 56}{space 2} .4273304{col 67}{space 1}   -0.21{col 76}{space 3}0.832{col 84}{space 4}-.9284147{col 97}{space 3} .7467903
{txt}{space 37}2006  {c |}{col 44}{res}{space 2}-.0017037{col 56}{space 2} .3733962{col 67}{space 1}   -0.00{col 76}{space 3}0.996{col 84}{space 4}-.7335909{col 97}{space 3} .7301834
{txt}{space 37}2007  {c |}{col 44}{res}{space 2} .2672902{col 56}{space 2} .4052524{col 67}{space 1}    0.66{col 76}{space 3}0.510{col 84}{space 4}-.5270377{col 97}{space 3} 1.061618
{txt}{space 37}2008  {c |}{col 44}{res}{space 2}-.0912303{col 56}{space 2} .3242844{col 67}{space 1}   -0.28{col 76}{space 3}0.778{col 84}{space 4}-.7268543{col 97}{space 3} .5443937
{txt}{space 37}2009  {c |}{col 44}{res}{space 2}-.4064256{col 56}{space 2} .3944252{col 67}{space 1}   -1.03{col 76}{space 3}0.303{col 84}{space 4}-1.179531{col 97}{space 3} .3666801
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2}-.1203919{col 56}{space 2} .0301908{col 67}{space 1}   -3.99{col 76}{space 3}0.000{col 84}{space 4}-.1795684{col 97}{space 3}-.0612155
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0064834{col 56}{space 2} .0360035{col 67}{space 1}    0.18{col 76}{space 3}0.857{col 84}{space 4}-.0640864{col 97}{space 3} .0770532
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0927864{col 56}{space 2} .0289062{col 67}{space 1}    3.21{col 76}{space 3}0.001{col 84}{space 4} .0361278{col 97}{space 3} .1494449
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0930149{col 56}{space 2} .0323451{col 67}{space 1}    2.88{col 76}{space 3}0.004{col 84}{space 4} .0296158{col 97}{space 3}  .156414
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0749636{col 56}{space 2} .0273614{col 67}{space 1}    2.74{col 76}{space 3}0.006{col 84}{space 4}  .021333{col 97}{space 3} .1285943
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .1031284{col 56}{space 2} .0415775{col 67}{space 1}    2.48{col 76}{space 3}0.013{col 84}{space 4} .0216331{col 97}{space 3} .1846237
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .1589675{col 56}{space 2} .0335603{col 67}{space 1}    4.74{col 76}{space 3}0.000{col 84}{space 4} .0931864{col 97}{space 3} .2247485
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0564979{col 56}{space 2}   .02264{col 67}{space 1}   -2.50{col 76}{space 3}0.013{col 84}{space 4}-.1008741{col 97}{space 3}-.0121216
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0363718{col 56}{space 2} .0336105{col 67}{space 1}    1.08{col 76}{space 3}0.279{col 84}{space 4}-.0295075{col 97}{space 3} .1022511
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0312104{col 56}{space 2} .0345575{col 67}{space 1}    0.90{col 76}{space 3}0.366{col 84}{space 4} -.036525{col 97}{space 3} .0989459
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2} .0491143{col 56}{space 2}  .033918{col 67}{space 1}    1.45{col 76}{space 3}0.148{col 84}{space 4}-.0173677{col 97}{space 3} .1155962
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0669853{col 56}{space 2} .0299611{col 67}{space 1}    2.24{col 76}{space 3}0.025{col 84}{space 4} .0082591{col 97}{space 3} .1257115
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .1034642{col 56}{space 2} .0324564{col 67}{space 1}    3.19{col 76}{space 3}0.001{col 84}{space 4} .0398469{col 97}{space 3} .1670815
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .1564861{col 56}{space 2} .0366734{col 67}{space 1}    4.27{col 76}{space 3}0.000{col 84}{space 4} .0846033{col 97}{space 3} .2283689
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2} .0072119{col 56}{space 2} .0284958{col 67}{space 1}    0.25{col 76}{space 3}0.800{col 84}{space 4}-.0486421{col 97}{space 3} .0630659
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2} .1361504{col 56}{space 2} .0439674{col 67}{space 1}    3.10{col 76}{space 3}0.002{col 84}{space 4} .0499706{col 97}{space 3} .2223302
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.1696374{col 56}{space 2} .0264424{col 67}{space 1}   -6.42{col 76}{space 3}0.000{col 84}{space 4}-.2214668{col 97}{space 3}-.1178081
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0914631{col 56}{space 2} .0324426{col 67}{space 1}   -2.82{col 76}{space 3}0.005{col 84}{space 4}-.1550532{col 97}{space 3} -.027873
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0703112{col 56}{space 2} .0286018{col 67}{space 1}    2.46{col 76}{space 3}0.014{col 84}{space 4} .0142493{col 97}{space 3}  .126373
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2}  .128113{col 56}{space 2} .0358694{col 67}{space 1}    3.57{col 76}{space 3}0.000{col 84}{space 4}  .057806{col 97}{space 3} .1984201
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0325395{col 56}{space 2} .0383361{col 67}{space 1}    0.85{col 76}{space 3}0.396{col 84}{space 4}-.0426025{col 97}{space 3} .1076814
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0541657{col 56}{space 2}  .036849{col 67}{space 1}    1.47{col 76}{space 3}0.142{col 84}{space 4}-.0180614{col 97}{space 3} .1263927
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0614057{col 56}{space 2} .0308964{col 67}{space 1}    1.99{col 76}{space 3}0.047{col 84}{space 4} .0008463{col 97}{space 3} .1219652
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0701796{col 56}{space 2} .0309894{col 67}{space 1}    2.26{col 76}{space 3}0.024{col 84}{space 4} .0094379{col 97}{space 3} .1309214
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0506172{col 56}{space 2} .0328634{col 67}{space 1}    1.54{col 76}{space 3}0.124{col 84}{space 4}-.0137977{col 97}{space 3}  .115032
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0469831{col 56}{space 2} .0319557{col 67}{space 1}    1.47{col 76}{space 3}0.142{col 84}{space 4}-.0156527{col 97}{space 3} .1096189
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .1089545{col 56}{space 2} .0333189{col 67}{space 1}    3.27{col 76}{space 3}0.001{col 84}{space 4} .0436467{col 97}{space 3} .1742623
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2} -.090873{col 56}{space 2} .0405498{col 67}{space 1}   -2.24{col 76}{space 3}0.025{col 84}{space 4} -.170354{col 97}{space 3}-.0113921
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2}  .052314{col 56}{space 2} .0329333{col 67}{space 1}    1.59{col 76}{space 3}0.112{col 84}{space 4} -.012238{col 97}{space 3} .1168659
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0357613{col 56}{space 2} .0224592{col 67}{space 1}    1.59{col 76}{space 3}0.111{col 84}{space 4}-.0082605{col 97}{space 3} .0797832
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0921257{col 56}{space 2} .0284305{col 67}{space 1}    3.24{col 76}{space 3}0.001{col 84}{space 4} .0363996{col 97}{space 3} .1478518
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0331223{col 56}{space 2} .0270106{col 67}{space 1}    1.23{col 76}{space 3}0.220{col 84}{space 4}-.0198207{col 97}{space 3} .0860652
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0560513{col 56}{space 2} .0299374{col 67}{space 1}   -1.87{col 76}{space 3}0.061{col 84}{space 4} -.114731{col 97}{space 3} .0026285
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0427884{col 56}{space 2} .0341228{col 67}{space 1}    1.25{col 76}{space 3}0.210{col 84}{space 4}-.0240952{col 97}{space 3} .1096719
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0839441{col 56}{space 2} .0464006{col 67}{space 1}    1.81{col 76}{space 3}0.070{col 84}{space 4}-.0070048{col 97}{space 3} .1748931
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2}   .02756{col 56}{space 2} .0276049{col 67}{space 1}    1.00{col 76}{space 3}0.318{col 84}{space 4}-.0265479{col 97}{space 3} .0816679
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .1023348{col 56}{space 2}  .031561{col 67}{space 1}    3.24{col 76}{space 3}0.001{col 84}{space 4} .0404726{col 97}{space 3}  .164197
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .0487754{col 56}{space 2} .0287568{col 67}{space 1}    1.70{col 76}{space 3}0.090{col 84}{space 4}-.0075903{col 97}{space 3} .1051411
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2}-.0324731{col 56}{space 2}  .038838{col 67}{space 1}   -0.84{col 76}{space 3}0.403{col 84}{space 4}-.1085988{col 97}{space 3} .0436527
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0482427{col 56}{space 2} .0290979{col 67}{space 1}    1.66{col 76}{space 3}0.097{col 84}{space 4}-.0087916{col 97}{space 3} .1052771
{txt}{space 36}soria  {c |}{col 44}{res}{space 2} .0543634{col 56}{space 2} .0480641{col 67}{space 1}    1.13{col 76}{space 3}0.258{col 84}{space 4}-.0398462{col 97}{space 3}  .148573
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0091324{col 56}{space 2} .0350442{col 67}{space 1}   -0.26{col 76}{space 3}0.794{col 84}{space 4}-.0778218{col 97}{space 3}  .059557
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2}-.0396988{col 56}{space 2} .0396087{col 67}{space 1}   -1.00{col 76}{space 3}0.316{col 84}{space 4}-.1173351{col 97}{space 3} .0379374
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .1308379{col 56}{space 2} .0343876{col 67}{space 1}    3.80{col 76}{space 3}0.000{col 84}{space 4} .0634353{col 97}{space 3} .1982405
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0649097{col 56}{space 2}  .025954{col 67}{space 1}    2.50{col 76}{space 3}0.012{col 84}{space 4} .0140378{col 97}{space 3} .1157817
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0387443{col 56}{space 2} .0299819{col 67}{space 1}    1.29{col 76}{space 3}0.196{col 84}{space 4}-.0200227{col 97}{space 3} .0975113
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.1313319{col 56}{space 2} .0235302{col 67}{space 1}   -5.58{col 76}{space 3}0.000{col 84}{space 4}-.1774531{col 97}{space 3}-.0852108
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2}  .177832{col 56}{space 2} .0409496{col 67}{space 1}    4.34{col 76}{space 3}0.000{col 84}{space 4} .0975675{col 97}{space 3} .2580965
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0278034{col 56}{space 2} .0312737{col 67}{space 1}    0.89{col 76}{space 3}0.374{col 84}{space 4}-.0334957{col 97}{space 3} .0891024
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0128998{col 56}{space 2} .0162928{col 67}{space 1}   -0.79{col 76}{space 3}0.429{col 84}{space 4} -.044835{col 97}{space 3} .0190354
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0171165{col 56}{space 2} .0153414{col 67}{space 1}   -1.12{col 76}{space 3}0.265{col 84}{space 4}-.0471869{col 97}{space 3} .0129539
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0089883{col 56}{space 2} .0166895{col 67}{space 1}   -0.54{col 76}{space 3}0.590{col 84}{space 4}-.0417012{col 97}{space 3} .0237245
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0054875{col 56}{space 2} .0151595{col 67}{space 1}   -0.36{col 76}{space 3}0.717{col 84}{space 4}-.0352013{col 97}{space 3} .0242264
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0025197{col 56}{space 2} .0219182{col 67}{space 1}   -0.11{col 76}{space 3}0.908{col 84}{space 4}-.0454811{col 97}{space 3} .0404417
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0247024{col 56}{space 2} .0191358{col 67}{space 1}    1.29{col 76}{space 3}0.197{col 84}{space 4}-.0128053{col 97}{space 3}   .06221
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0121249{col 56}{space 2} .0061407{col 67}{space 1}   -1.97{col 76}{space 3}0.048{col 84}{space 4}-.0241612{col 97}{space 3}-.0000887
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0008952{col 56}{space 2}  .000302{col 67}{space 1}    2.96{col 76}{space 3}0.003{col 84}{space 4} .0003033{col 97}{space 3}  .001487
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0295995{col 56}{space 2} .0102697{col 67}{space 1}   -2.88{col 76}{space 3}0.004{col 84}{space 4}-.0497289{col 97}{space 3}-.0094701
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0234485{col 56}{space 2}  .013954{col 67}{space 1}    1.68{col 76}{space 3}0.093{col 84}{space 4}-.0039025{col 97}{space 3} .0507995
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0165025{col 56}{space 2} .0102119{col 67}{space 1}   -1.62{col 76}{space 3}0.106{col 84}{space 4}-.0365186{col 97}{space 3} .0035136
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0269974{col 56}{space 2} .0152911{col 67}{space 1}    1.77{col 76}{space 3}0.077{col 84}{space 4}-.0029745{col 97}{space 3} .0569693
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2087025{col 56}{space 2} .0371785{col 67}{space 1}    5.61{col 76}{space 3}0.000{col 84}{space 4} .1358296{col 97}{space 3} .2815755
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}34{txt}, {res}20140{txt}) = {res}1.264{col 62}{txt} Prob > F = {res}0.139

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pre-10}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    1.6114
{col 25}{txt}Prob>|t| = {res}    0.1441

95%{txt} confidence set for null hypothesis expression: {res}[−.01243, .06812]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:20,260}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2438796{col 28}{space 2}  .003017{col 39}{space 5}  .237966{col 53}{space 3} .2497931
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:20,905}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:21}
{txt}{col 53}{lalign 17:F({res:75}, {res:20809})}{col 70} = {res}{ralign 6:5.02}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0252}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0207}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.3746}

{txt}{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  Std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0053066{col 56}{space 2} .0100596{col 67}{space 1}    0.53{col 76}{space 3}0.598{col 84}{space 4}-.0144111{col 97}{space 3} .0250243
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0617172{col 56}{space 2} .2448171{col 67}{space 1}   -0.25{col 76}{space 3}0.801{col 84}{space 4}-.5415777{col 97}{space 3} .4181434
{txt}{space 42} {c |}
{space 29}year_original {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2012  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2013  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2014  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2015  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2016  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2017  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2018  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2019  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2020  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2021  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 37}2022  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 6}year_original#c.expenditure_gdp_ours {c |}
{space 37}2011  {c |}{col 44}{res}{space 2}-.3278811{col 56}{space 2} .3133421{col 67}{space 1}   -1.05{col 76}{space 3}0.295{col 84}{space 4} -.942056{col 97}{space 3} .2862938
{txt}{space 37}2012  {c |}{col 44}{res}{space 2} .6045103{col 56}{space 2} .3107237{col 67}{space 1}    1.95{col 76}{space 3}0.052{col 84}{space 4}-.0045323{col 97}{space 3} 1.213553
{txt}{space 37}2013  {c |}{col 44}{res}{space 2} .1079999{col 56}{space 2} .2908137{col 67}{space 1}    0.37{col 76}{space 3}0.710{col 84}{space 4}-.4620177{col 97}{space 3} .6780174
{txt}{space 37}2014  {c |}{col 44}{res}{space 2}-.3378774{col 56}{space 2} .3073314{col 67}{space 1}   -1.10{col 76}{space 3}0.272{col 84}{space 4} -.940271{col 97}{space 3} .2645161
{txt}{space 37}2015  {c |}{col 44}{res}{space 2} .0765286{col 56}{space 2} .3106262{col 67}{space 1}    0.25{col 76}{space 3}0.805{col 84}{space 4}-.5323231{col 97}{space 3} .6853802
{txt}{space 37}2016  {c |}{col 44}{res}{space 2}-.2075895{col 56}{space 2} .2701596{col 67}{space 1}   -0.77{col 76}{space 3}0.442{col 84}{space 4}-.7371234{col 97}{space 3} .3219444
{txt}{space 37}2017  {c |}{col 44}{res}{space 2} .1590327{col 56}{space 2} .2907447{col 67}{space 1}    0.55{col 76}{space 3}0.584{col 84}{space 4}-.4108496{col 97}{space 3} .7289151
{txt}{space 37}2018  {c |}{col 44}{res}{space 2} .1033493{col 56}{space 2} .2848357{col 67}{space 1}    0.36{col 76}{space 3}0.717{col 84}{space 4}-.4549508{col 97}{space 3} .6616495
{txt}{space 37}2019  {c |}{col 44}{res}{space 2}-.0222855{col 56}{space 2} .2140679{col 67}{space 1}   -0.10{col 76}{space 3}0.917{col 84}{space 4}-.4418753{col 97}{space 3} .3973044
{txt}{space 37}2020  {c |}{col 44}{res}{space 2} .0565766{col 56}{space 2} .2260699{col 67}{space 1}    0.25{col 76}{space 3}0.802{col 84}{space 4} -.386538{col 97}{space 3} .4996911
{txt}{space 37}2021  {c |}{col 44}{res}{space 2}-.2697605{col 56}{space 2} .2211268{col 67}{space 1}   -1.22{col 76}{space 3}0.223{col 84}{space 4}-.7031862{col 97}{space 3} .1636652
{txt}{space 37}2022  {c |}{col 44}{res}{space 2}-.0789084{col 56}{space 2} .2138186{col 67}{space 1}   -0.37{col 76}{space 3}0.712{col 84}{space 4}-.4980095{col 97}{space 3} .3401927
{txt}{space 42} {c |}
{space 34}prov_num {c |}
{space 36}alava  {c |}{col 44}{res}{space 2} -.053026{col 56}{space 2} .0373926{col 67}{space 1}   -1.42{col 76}{space 3}0.156{col 84}{space 4}-.1263185{col 97}{space 3} .0202665
{txt}{space 33}albacete  {c |}{col 44}{res}{space 2} .0433416{col 56}{space 2} .0356787{col 67}{space 1}    1.21{col 76}{space 3}0.224{col 84}{space 4}-.0265916{col 97}{space 3} .1132747
{txt}{space 33}alicante  {c |}{col 44}{res}{space 2} .0511098{col 56}{space 2} .0245417{col 67}{space 1}    2.08{col 76}{space 3}0.037{col 84}{space 4} .0030061{col 97}{space 3} .0992135
{txt}{space 34}almeria  {c |}{col 44}{res}{space 2} .0418774{col 56}{space 2} .0298819{col 67}{space 1}    1.40{col 76}{space 3}0.161{col 84}{space 4}-.0166934{col 97}{space 3} .1004482
{txt}{space 33}asturias  {c |}{col 44}{res}{space 2} .0209943{col 56}{space 2} .0286546{col 67}{space 1}    0.73{col 76}{space 3}0.464{col 84}{space 4}-.0351709{col 97}{space 3} .0771595
{txt}{space 36}avila  {c |}{col 44}{res}{space 2} .0305893{col 56}{space 2} .0464084{col 67}{space 1}    0.66{col 76}{space 3}0.510{col 84}{space 4}-.0603747{col 97}{space 3} .1215533
{txt}{space 34}badajoz  {c |}{col 44}{res}{space 2} .0117522{col 56}{space 2} .0294477{col 67}{space 1}    0.40{col 76}{space 3}0.690{col 84}{space 4}-.0459676{col 97}{space 3} .0694721
{txt}{space 32}barcelona  {c |}{col 44}{res}{space 2}-.0102362{col 56}{space 2} .0263553{col 67}{space 1}   -0.39{col 76}{space 3}0.698{col 84}{space 4}-.0618946{col 97}{space 3} .0414222
{txt}{space 35}burgos  {c |}{col 44}{res}{space 2} .0573814{col 56}{space 2} .0382134{col 67}{space 1}    1.50{col 76}{space 3}0.133{col 84}{space 4}-.0175198{col 97}{space 3} .1322826
{txt}{space 34}caceres  {c |}{col 44}{res}{space 2} .0966823{col 56}{space 2} .0354124{col 67}{space 1}    2.73{col 76}{space 3}0.006{col 84}{space 4} .0272713{col 97}{space 3} .1660933
{txt}{space 36}cadiz  {c |}{col 44}{res}{space 2}  .040487{col 56}{space 2} .0264797{col 67}{space 1}    1.53{col 76}{space 3}0.126{col 84}{space 4}-.0114153{col 97}{space 3} .0923894
{txt}{space 32}cantabria  {c |}{col 44}{res}{space 2} .0311623{col 56}{space 2} .0260572{col 67}{space 1}    1.20{col 76}{space 3}0.232{col 84}{space 4}-.0199118{col 97}{space 3} .0822364
{txt}{space 32}castellon  {c |}{col 44}{res}{space 2} .0123032{col 56}{space 2} .0296109{col 67}{space 1}    0.42{col 76}{space 3}0.678{col 84}{space 4}-.0457364{col 97}{space 3} .0703428
{txt}{space 30}ciudad real  {c |}{col 44}{res}{space 2} .0680672{col 56}{space 2} .0340335{col 67}{space 1}    2.00{col 76}{space 3}0.046{col 84}{space 4} .0013589{col 97}{space 3} .1347756
{txt}{space 34}cordoba  {c |}{col 44}{res}{space 2}-.0287475{col 56}{space 2} .0293638{col 67}{space 1}   -0.98{col 76}{space 3}0.328{col 84}{space 4}-.0863027{col 97}{space 3} .0288078
{txt}{space 35}cuenca  {c |}{col 44}{res}{space 2}-.0348369{col 56}{space 2} .0491729{col 67}{space 1}   -0.71{col 76}{space 3}0.479{col 84}{space 4}-.1312197{col 97}{space 3} .0615459
{txt}{space 33}gipuzkoa  {c |}{col 44}{res}{space 2}-.0960765{col 56}{space 2}  .030729{col 67}{space 1}   -3.13{col 76}{space 3}0.002{col 84}{space 4}-.1563077{col 97}{space 3}-.0358454
{txt}{space 35}girona  {c |}{col 44}{res}{space 2}-.0728111{col 56}{space 2} .0349485{col 67}{space 1}   -2.08{col 76}{space 3}0.037{col 84}{space 4}-.1413129{col 97}{space 3}-.0043093
{txt}{space 34}granada  {c |}{col 44}{res}{space 2} .0303906{col 56}{space 2} .0265762{col 67}{space 1}    1.14{col 76}{space 3}0.253{col 84}{space 4}-.0217008{col 97}{space 3}  .082482
{txt}{space 30}guadalajara  {c |}{col 44}{res}{space 2} .1083173{col 56}{space 2} .0390513{col 67}{space 1}    2.77{col 76}{space 3}0.006{col 84}{space 4} .0317737{col 97}{space 3}  .184861
{txt}{space 35}huelva  {c |}{col 44}{res}{space 2} .0519747{col 56}{space 2} .0379791{col 67}{space 1}    1.37{col 76}{space 3}0.171{col 84}{space 4}-.0224673{col 97}{space 3} .1264167
{txt}{space 35}huesca  {c |}{col 44}{res}{space 2} .0452602{col 56}{space 2} .0436491{col 67}{space 1}    1.04{col 76}{space 3}0.300{col 84}{space 4}-.0402953{col 97}{space 3} .1308158
{txt}{space 27}islas baleares  {c |}{col 44}{res}{space 2} .0385978{col 56}{space 2} .0329686{col 67}{space 1}    1.17{col 76}{space 3}0.242{col 84}{space 4}-.0260233{col 97}{space 3} .1032189
{txt}{space 37}jaen  {c |}{col 44}{res}{space 2} .0632479{col 56}{space 2} .0300447{col 67}{space 1}    2.11{col 76}{space 3}0.035{col 84}{space 4}  .004358{col 97}{space 3} .1221378
{txt}{space 33}la rioja  {c |}{col 44}{res}{space 2} .0708675{col 56}{space 2} .0308923{col 67}{space 1}    2.29{col 76}{space 3}0.022{col 84}{space 4} .0103162{col 97}{space 3} .1314189
{txt}{space 31}las palmas  {c |}{col 44}{res}{space 2} .0183131{col 56}{space 2} .0301122{col 67}{space 1}    0.61{col 76}{space 3}0.543{col 84}{space 4}-.0407091{col 97}{space 3} .0773353
{txt}{space 37}leon  {c |}{col 44}{res}{space 2} .0705216{col 56}{space 2} .0359625{col 67}{space 1}    1.96{col 76}{space 3}0.050{col 84}{space 4} .0000323{col 97}{space 3}  .141011
{txt}{space 35}lleida  {c |}{col 44}{res}{space 2}-.0143485{col 56}{space 2} .0332844{col 67}{space 1}   -0.43{col 76}{space 3}0.666{col 84}{space 4}-.0795885{col 97}{space 3} .0508916
{txt}{space 37}lugo  {c |}{col 44}{res}{space 2} .0091082{col 56}{space 2} .0355355{col 67}{space 1}    0.26{col 76}{space 3}0.798{col 84}{space 4}-.0605441{col 97}{space 3} .0787606
{txt}{space 35}madrid  {c |}{col 44}{res}{space 2} .0270568{col 56}{space 2}  .022198{col 67}{space 1}    1.22{col 76}{space 3}0.223{col 84}{space 4}-.0164531{col 97}{space 3} .0705666
{txt}{space 35}malaga  {c |}{col 44}{res}{space 2} .0275661{col 56}{space 2}  .023908{col 67}{space 1}    1.15{col 76}{space 3}0.249{col 84}{space 4}-.0192955{col 97}{space 3} .0744276
{txt}{space 35}murcia  {c |}{col 44}{res}{space 2} .0097093{col 56}{space 2} .0242841{col 67}{space 1}    0.40{col 76}{space 3}0.689{col 84}{space 4}-.0378893{col 97}{space 3} .0573079
{txt}{space 34}navarra  {c |}{col 44}{res}{space 2}-.0188799{col 56}{space 2} .0300927{col 67}{space 1}   -0.63{col 76}{space 3}0.530{col 84}{space 4}-.0778639{col 97}{space 3} .0401042
{txt}{space 34}ourense  {c |}{col 44}{res}{space 2} .0423893{col 56}{space 2} .0399851{col 67}{space 1}    1.06{col 76}{space 3}0.289{col 84}{space 4}-.0359846{col 97}{space 3} .1207632
{txt}{space 33}palencia  {c |}{col 44}{res}{space 2} .0384168{col 56}{space 2}  .051484{col 67}{space 1}    0.75{col 76}{space 3}0.456{col 84}{space 4}-.0624958{col 97}{space 3} .1393294
{txt}{space 31}pontevedra  {c |}{col 44}{res}{space 2} .0236925{col 56}{space 2} .0261946{col 67}{space 1}    0.90{col 76}{space 3}0.366{col 84}{space 4}-.0276509{col 97}{space 3} .0750359
{txt}{space 32}salamanca  {c |}{col 44}{res}{space 2} .0758067{col 56}{space 2} .0357213{col 67}{space 1}    2.12{col 76}{space 3}0.034{col 84}{space 4} .0057902{col 97}{space 3} .1458231
{txt}{space 19}santa cruz de tenerife  {c |}{col 44}{res}{space 2} .1045674{col 56}{space 2} .0257119{col 67}{space 1}    4.07{col 76}{space 3}0.000{col 84}{space 4} .0541702{col 97}{space 3} .1549647
{txt}{space 34}segovia  {c |}{col 44}{res}{space 2} .0348347{col 56}{space 2} .0581769{col 67}{space 1}    0.60{col 76}{space 3}0.549{col 84}{space 4}-.0791966{col 97}{space 3}  .148866
{txt}{space 34}sevilla  {c |}{col 44}{res}{space 2} .0193537{col 56}{space 2} .0254829{col 67}{space 1}    0.76{col 76}{space 3}0.448{col 84}{space 4}-.0305947{col 97}{space 3} .0693021
{txt}{space 36}soria  {c |}{col 44}{res}{space 2}  .179892{col 56}{space 2} .0973811{col 67}{space 1}    1.85{col 76}{space 3}0.065{col 84}{space 4}-.0109826{col 97}{space 3} .3707666
{txt}{space 32}tarragona  {c |}{col 44}{res}{space 2}-.0313661{col 56}{space 2} .0341114{col 67}{space 1}   -0.92{col 76}{space 3}0.358{col 84}{space 4}-.0982271{col 97}{space 3}  .035495
{txt}{space 35}teruel  {c |}{col 44}{res}{space 2} .0936143{col 56}{space 2} .0550621{col 67}{space 1}    1.70{col 76}{space 3}0.089{col 84}{space 4}-.0143117{col 97}{space 3} .2015402
{txt}{space 35}toledo  {c |}{col 44}{res}{space 2} .0315546{col 56}{space 2} .0303381{col 67}{space 1}    1.04{col 76}{space 3}0.298{col 84}{space 4}-.0279105{col 97}{space 3} .0910196
{txt}{space 33}valencia  {c |}{col 44}{res}{space 2} .0476235{col 56}{space 2} .0214996{col 67}{space 1}    2.22{col 76}{space 3}0.027{col 84}{space 4} .0054826{col 97}{space 3} .0897643
{txt}{space 31}valladolid  {c |}{col 44}{res}{space 2} .0200424{col 56}{space 2}  .026109{col 67}{space 1}    0.77{col 76}{space 3}0.443{col 84}{space 4}-.0311332{col 97}{space 3} .0712181
{txt}{space 34}vizcaya  {c |}{col 44}{res}{space 2}-.0604527{col 56}{space 2} .0230668{col 67}{space 1}   -2.62{col 76}{space 3}0.009{col 84}{space 4}-.1056654{col 97}{space 3}-.0152401
{txt}{space 35}zamora  {c |}{col 44}{res}{space 2} .0297202{col 56}{space 2} .0498318{col 67}{space 1}    0.60{col 76}{space 3}0.551{col 84}{space 4} -.067954{col 97}{space 3} .1273945
{txt}{space 33}zaragoza  {c |}{col 44}{res}{space 2} .0225966{col 56}{space 2} .0262241{col 67}{space 1}    0.86{col 76}{space 3}0.389{col 84}{space 4}-.0288048{col 97}{space 3} .0739979
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0036694{col 56}{space 2} .0153067{col 67}{space 1}   -0.24{col 76}{space 3}0.811{col 84}{space 4}-.0336716{col 97}{space 3} .0263329
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0007588{col 56}{space 2} .0144926{col 67}{space 1}   -0.05{col 76}{space 3}0.958{col 84}{space 4}-.0291655{col 97}{space 3} .0276479
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0144499{col 56}{space 2} .0153486{col 67}{space 1}    0.94{col 76}{space 3}0.346{col 84}{space 4}-.0156347{col 97}{space 3} .0445344
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0113926{col 56}{space 2} .0143997{col 67}{space 1}    0.79{col 76}{space 3}0.429{col 84}{space 4} -.016832{col 97}{space 3} .0396172
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0100782{col 56}{space 2}  .018151{col 67}{space 1}   -0.56{col 76}{space 3}0.579{col 84}{space 4}-.0456556{col 97}{space 3} .0254991
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0017236{col 56}{space 2} .0167588{col 67}{space 1}   -0.10{col 76}{space 3}0.918{col 84}{space 4}-.0345722{col 97}{space 3} .0311249
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0250007{col 56}{space 2} .0052719{col 67}{space 1}    4.74{col 76}{space 3}0.000{col 84}{space 4} .0146675{col 97}{space 3}  .035334
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0016547{col 56}{space 2}  .000258{col 67}{space 1}    6.41{col 76}{space 3}0.000{col 84}{space 4} .0011489{col 97}{space 3} .0021605
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0058425{col 56}{space 2} .0089513{col 67}{space 1}    0.65{col 76}{space 3}0.514{col 84}{space 4}-.0117027{col 97}{space 3} .0233876
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0334093{col 56}{space 2} .0097474{col 67}{space 1}    3.43{col 76}{space 3}0.001{col 84}{space 4} .0143037{col 97}{space 3}  .052515
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0433675{col 56}{space 2} .0155932{col 67}{space 1}    2.78{col 76}{space 3}0.005{col 84}{space 4} .0128036{col 97}{space 3} .0739313
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0075316{col 56}{space 2} .0222193{col 67}{space 1}   -0.34{col 76}{space 3}0.735{col 84}{space 4}-.0510831{col 97}{space 3} .0360199
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .0862727{col 56}{space 2} .0496115{col 67}{space 1}    1.74{col 76}{space 3}0.082{col 84}{space 4}-.0109697{col 97}{space 3} .1835151
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}20{txt}, {res}20809{txt}) = {res}2.434{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\times$}"

added macro:
       e(PeriodCtrols) : "{res:$\times$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Post-09}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.6106
{col 25}{txt}Prob>|t| = {res}    0.5375

95%{txt} confidence set for null hypothesis expression: {res}[−.01564, .02665]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:20,905}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .1733557{col 28}{space 2} .0026183{col 39}{space 5} .1682237{col 53}{space 3} .1784877
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{p 0 6 2}note: {bf:1988.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1989.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1990.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1991.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1992.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1993.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1994.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1995.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1996.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1997.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1998.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1999.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2000.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2001.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2002.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2003.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2004.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2005.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2006.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2007.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2008.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2009.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2010.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2011.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2012.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2013.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2014.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2015.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2016.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2017.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2018.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2019.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2020.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2021.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:2022.year_original} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:preperiod} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#7.municipality_size} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#0b.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#1.female} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:1.preperiod#5.status} omitted because of collinearity.{p_end}
{p 0 6 2}note: {bf:50.prov_num#1.preperiod} omitted because of collinearity.{p_end}
{res}
{txt}{col 1}Linear regression, absorbing indicators{col 53}{lalign 17:Number of obs}{col 70} = {res}{ralign 6:41,165}
{txt}{col 1}Absorbed variable: {res:survey}{col 53}{lalign 17:No. of categories}{col 70} = {res}{ralign 6:56}
{txt}{col 53}{lalign 17:F({res:159}, {res:40950})}{col 70} = {res}{ralign 6:7.52}
{txt}{col 53}{lalign 17:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 53}{lalign 17:R-squared}{col 70} = {res}{ralign 6:0.0538}
{txt}{col 53}{lalign 17:Adj R-squared}{col 70} = {res}{ralign 6:0.0489}
{txt}{col 53}{lalign 17:Root MSE}{col 70} = {res}{ralign 6:0.3959}

{txt}{hline 45}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                              vote_incumbent{col 46}{c |} Coefficient{col 58}  Std. err.{col 70}      t{col 78}   P>|t|{col 86}     [95% con{col 99}f. interval]
{hline 45}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}top_prizes_gdp_ours {c |}{col 46}{res}{space 2} .0151212{col 58}{space 2} .0079685{col 69}{space 1}    1.90{col 78}{space 3}0.058{col 86}{space 4}-.0004973{col 99}{space 3} .0307397
{txt}{space 24}expenditure_gdp_ours {c |}{col 46}{res}{space 2} .1589102{col 58}{space 2} .3083639{col 69}{space 1}    0.52{col 78}{space 3}0.606{col 86}{space 4}-.4454899{col 99}{space 3} .7633102
{txt}{space 44} {c |}
{space 31}year_original {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1989  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1990  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1991  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1992  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1993  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1994  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1995  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1996  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1997  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1998  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}1999  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2000  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2001  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2002  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2003  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2004  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2005  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2006  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2007  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2008  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2009  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2010  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2011  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2012  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2013  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2014  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2015  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2016  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2017  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2018  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2019  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2020  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2021  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 39}2022  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 8}year_original#c.expenditure_gdp_ours {c |}
{space 39}1988  {c |}{col 46}{res}{space 2}-.3762385{col 58}{space 2} .4789566{col 69}{space 1}   -0.79{col 78}{space 3}0.432{col 86}{space 4}-1.315004{col 99}{space 3} .5625269
{txt}{space 39}1989  {c |}{col 46}{res}{space 2}-.4131386{col 58}{space 2} .3795508{col 69}{space 1}   -1.09{col 78}{space 3}0.276{col 86}{space 4}-1.157066{col 99}{space 3} .3307893
{txt}{space 39}1990  {c |}{col 46}{res}{space 2}-.5444587{col 58}{space 2}  .357168{col 69}{space 1}   -1.52{col 78}{space 3}0.127{col 86}{space 4}-1.244516{col 99}{space 3} .1555984
{txt}{space 39}1991  {c |}{col 46}{res}{space 2}-.7361632{col 58}{space 2} .4802628{col 69}{space 1}   -1.53{col 78}{space 3}0.125{col 86}{space 4}-1.677489{col 99}{space 3} .2051624
{txt}{space 39}1992  {c |}{col 46}{res}{space 2}   -.7691{col 58}{space 2} .4015251{col 69}{space 1}   -1.92{col 78}{space 3}0.055{col 86}{space 4}-1.556098{col 99}{space 3}  .017898
{txt}{space 39}1993  {c |}{col 46}{res}{space 2}-.3903731{col 58}{space 2} .3648827{col 69}{space 1}   -1.07{col 78}{space 3}0.285{col 86}{space 4}-1.105551{col 99}{space 3}  .324805
{txt}{space 39}1994  {c |}{col 46}{res}{space 2}-.4600764{col 58}{space 2} .3642054{col 69}{space 1}   -1.26{col 78}{space 3}0.207{col 86}{space 4}-1.173927{col 99}{space 3} .2537743
{txt}{space 39}1995  {c |}{col 46}{res}{space 2}-.5294314{col 58}{space 2} .3546992{col 69}{space 1}   -1.49{col 78}{space 3}0.136{col 86}{space 4} -1.22465{col 99}{space 3} .1657869
{txt}{space 39}1996  {c |}{col 46}{res}{space 2}-.4917703{col 58}{space 2} .3880455{col 69}{space 1}   -1.27{col 78}{space 3}0.205{col 86}{space 4}-1.252348{col 99}{space 3} .2688073
{txt}{space 39}1997  {c |}{col 46}{res}{space 2}-.2287918{col 58}{space 2} .4394936{col 69}{space 1}   -0.52{col 78}{space 3}0.603{col 86}{space 4}-1.090209{col 99}{space 3} .6326252
{txt}{space 39}1998  {c |}{col 46}{res}{space 2} .3759732{col 58}{space 2} .4600838{col 69}{space 1}    0.82{col 78}{space 3}0.414{col 86}{space 4}-.5258012{col 99}{space 3} 1.277748
{txt}{space 39}1999  {c |}{col 46}{res}{space 2} .5118194{col 58}{space 2} .4510964{col 69}{space 1}    1.13{col 78}{space 3}0.257{col 86}{space 4}-.3723395{col 99}{space 3} 1.395978
{txt}{space 39}2000  {c |}{col 46}{res}{space 2}-.0094301{col 58}{space 2} .3142114{col 69}{space 1}   -0.03{col 78}{space 3}0.976{col 86}{space 4}-.6252914{col 99}{space 3} .6064312
{txt}{space 39}2001  {c |}{col 46}{res}{space 2}-.2608219{col 58}{space 2} .4349414{col 69}{space 1}   -0.60{col 78}{space 3}0.549{col 86}{space 4}-1.113317{col 99}{space 3} .5916729
{txt}{space 39}2002  {c |}{col 46}{res}{space 2}-.6085724{col 58}{space 2} .4438594{col 69}{space 1}   -1.37{col 78}{space 3}0.170{col 86}{space 4}-1.478547{col 99}{space 3} .2614016
{txt}{space 39}2003  {c |}{col 46}{res}{space 2}-.6259501{col 58}{space 2} .4085195{col 69}{space 1}   -1.53{col 78}{space 3}0.125{col 86}{space 4}-1.426657{col 99}{space 3}  .174757
{txt}{space 39}2004  {c |}{col 46}{res}{space 2} -.136258{col 58}{space 2} .3116561{col 69}{space 1}   -0.44{col 78}{space 3}0.662{col 86}{space 4}-.7471109{col 99}{space 3} .4745948
{txt}{space 39}2005  {c |}{col 46}{res}{space 2}  -.08728{col 58}{space 2} .4059647{col 69}{space 1}   -0.21{col 78}{space 3}0.830{col 86}{space 4}-.8829796{col 99}{space 3} .7084196
{txt}{space 39}2006  {c |}{col 46}{res}{space 2}-.0085133{col 58}{space 2} .3547004{col 69}{space 1}   -0.02{col 78}{space 3}0.981{col 86}{space 4}-.7037339{col 99}{space 3} .6867072
{txt}{space 39}2007  {c |}{col 46}{res}{space 2} .2604447{col 58}{space 2} .3849667{col 69}{space 1}    0.68{col 78}{space 3}0.499{col 86}{space 4}-.4940985{col 99}{space 3} 1.014988
{txt}{space 39}2008  {c |}{col 46}{res}{space 2}-.0925706{col 58}{space 2} .3080753{col 69}{space 1}   -0.30{col 78}{space 3}0.764{col 86}{space 4}-.6964049{col 99}{space 3} .5112638
{txt}{space 39}2009  {c |}{col 46}{res}{space 2}-.4173627{col 58}{space 2} .3746299{col 69}{space 1}   -1.11{col 78}{space 3}0.265{col 86}{space 4}-1.151646{col 99}{space 3} .3169202
{txt}{space 39}2010  {c |}{col 46}{res}{space 2}-.2112893{col 58}{space 2}  .402411{col 69}{space 1}   -0.53{col 78}{space 3}0.600{col 86}{space 4}-1.000024{col 99}{space 3} .5774451
{txt}{space 39}2011  {c |}{col 46}{res}{space 2}-.5425195{col 58}{space 2} .4206727{col 69}{space 1}   -1.29{col 78}{space 3}0.197{col 86}{space 4}-1.367047{col 99}{space 3} .2820083
{txt}{space 39}2012  {c |}{col 46}{res}{space 2} .3831616{col 58}{space 2} .4231432{col 69}{space 1}    0.91{col 78}{space 3}0.365{col 86}{space 4}-.4462082{col 99}{space 3} 1.212532
{txt}{space 39}2013  {c |}{col 46}{res}{space 2}-.1155781{col 58}{space 2} .4060468{col 69}{space 1}   -0.28{col 78}{space 3}0.776{col 86}{space 4}-.9114386{col 99}{space 3} .6802825
{txt}{space 39}2014  {c |}{col 46}{res}{space 2}-.5498253{col 58}{space 2} .4239614{col 69}{space 1}   -1.30{col 78}{space 3}0.195{col 86}{space 4}-1.380799{col 99}{space 3} .2811484
{txt}{space 39}2015  {c |}{col 46}{res}{space 2}-.1340951{col 58}{space 2}  .428152{col 69}{space 1}   -0.31{col 78}{space 3}0.754{col 86}{space 4}-.9732824{col 99}{space 3} .7050922
{txt}{space 39}2016  {c |}{col 46}{res}{space 2}-.4231798{col 58}{space 2} .3928413{col 69}{space 1}   -1.08{col 78}{space 3}0.281{col 86}{space 4}-1.193157{col 99}{space 3} .3467977
{txt}{space 39}2017  {c |}{col 46}{res}{space 2}-.0558996{col 58}{space 2} .4096333{col 69}{space 1}   -0.14{col 78}{space 3}0.891{col 86}{space 4}-.8587898{col 99}{space 3} .7469905
{txt}{space 39}2018  {c |}{col 46}{res}{space 2}-.1196142{col 58}{space 2}  .406479{col 69}{space 1}   -0.29{col 78}{space 3}0.769{col 86}{space 4} -.916322{col 99}{space 3} .6770936
{txt}{space 39}2019  {c |}{col 46}{res}{space 2}-.2473518{col 58}{space 2} .3564475{col 69}{space 1}   -0.69{col 78}{space 3}0.488{col 86}{space 4}-.9459968{col 99}{space 3} .4512931
{txt}{space 39}2020  {c |}{col 46}{res}{space 2}-.1540449{col 58}{space 2} .3635656{col 69}{space 1}   -0.42{col 78}{space 3}0.672{col 86}{space 4}-.8666414{col 99}{space 3} .5585515
{txt}{space 39}2021  {c |}{col 46}{res}{space 2}-.4815548{col 58}{space 2} .3658383{col 69}{space 1}   -1.32{col 78}{space 3}0.188{col 86}{space 4}-1.198606{col 99}{space 3} .2354964
{txt}{space 39}2022  {c |}{col 46}{res}{space 2}-.2914173{col 58}{space 2} .3451755{col 69}{space 1}   -0.84{col 78}{space 3}0.399{col 86}{space 4}-.9679689{col 99}{space 3} .3851342
{txt}{space 44} {c |}
{space 36}prov_num {c |}
{space 38}alava  {c |}{col 46}{res}{space 2}-.0526223{col 58}{space 2} .0395145{col 69}{space 1}   -1.33{col 78}{space 3}0.183{col 86}{space 4}-.1300715{col 99}{space 3} .0248269
{txt}{space 35}albacete  {c |}{col 46}{res}{space 2} .0317392{col 58}{space 2} .0367753{col 69}{space 1}    0.86{col 78}{space 3}0.388{col 86}{space 4}-.0403411{col 99}{space 3} .1038195
{txt}{space 35}alicante  {c |}{col 46}{res}{space 2} .0501293{col 58}{space 2} .0259255{col 69}{space 1}    1.93{col 78}{space 3}0.053{col 86}{space 4}-.0006852{col 99}{space 3} .1009439
{txt}{space 36}almeria  {c |}{col 46}{res}{space 2} .0402624{col 58}{space 2} .0315571{col 69}{space 1}    1.28{col 78}{space 3}0.202{col 86}{space 4}-.0215903{col 99}{space 3}  .102115
{txt}{space 35}asturias  {c |}{col 46}{res}{space 2} .0206096{col 58}{space 2} .0302801{col 69}{space 1}    0.68{col 78}{space 3}0.496{col 86}{space 4}-.0387401{col 99}{space 3} .0799593
{txt}{space 38}avila  {c |}{col 46}{res}{space 2} .0304988{col 58}{space 2} .0490431{col 69}{space 1}    0.62{col 78}{space 3}0.534{col 86}{space 4}-.0656266{col 99}{space 3} .1266243
{txt}{space 36}badajoz  {c |}{col 46}{res}{space 2} .0118347{col 58}{space 2} .0311195{col 69}{space 1}    0.38{col 78}{space 3}0.704{col 86}{space 4}-.0491602{col 99}{space 3} .0728297
{txt}{space 34}barcelona  {c |}{col 46}{res}{space 2}-.0096556{col 58}{space 2} .0278484{col 69}{space 1}   -0.35{col 78}{space 3}0.729{col 86}{space 4}-.0642391{col 99}{space 3}  .044928
{txt}{space 37}burgos  {c |}{col 46}{res}{space 2} .0557205{col 58}{space 2} .0403653{col 69}{space 1}    1.38{col 78}{space 3}0.167{col 86}{space 4}-.0233964{col 99}{space 3} .1348373
{txt}{space 36}caceres  {c |}{col 46}{res}{space 2} .0964713{col 58}{space 2} .0374225{col 69}{space 1}    2.58{col 78}{space 3}0.010{col 86}{space 4} .0231224{col 99}{space 3} .1698203
{txt}{space 38}cadiz  {c |}{col 46}{res}{space 2}  .040178{col 58}{space 2} .0279822{col 69}{space 1}    1.44{col 78}{space 3}0.151{col 86}{space 4}-.0146678{col 99}{space 3} .0950237
{txt}{space 34}cantabria  {c |}{col 46}{res}{space 2} .0309632{col 58}{space 2} .0275362{col 69}{space 1}    1.12{col 78}{space 3}0.261{col 86}{space 4}-.0230083{col 99}{space 3} .0849346
{txt}{space 34}castellon  {c |}{col 46}{res}{space 2} .0123575{col 58}{space 2} .0312919{col 69}{space 1}    0.39{col 78}{space 3}0.693{col 86}{space 4}-.0489753{col 99}{space 3} .0736904
{txt}{space 32}ciudad real  {c |}{col 46}{res}{space 2} .0678626{col 58}{space 2} .0359654{col 69}{space 1}    1.89{col 78}{space 3}0.059{col 86}{space 4}-.0026304{col 99}{space 3} .1383556
{txt}{space 36}cordoba  {c |}{col 46}{res}{space 2}-.0286981{col 58}{space 2} .0310308{col 69}{space 1}   -0.92{col 78}{space 3}0.355{col 86}{space 4}-.0895191{col 99}{space 3}  .032123
{txt}{space 37}cuenca  {c |}{col 46}{res}{space 2}-.0444419{col 58}{space 2} .0515063{col 69}{space 1}   -0.86{col 78}{space 3}0.388{col 86}{space 4}-.1453954{col 99}{space 3} .0565116
{txt}{space 35}gipuzkoa  {c |}{col 46}{res}{space 2}-.0954907{col 58}{space 2} .0324708{col 69}{space 1}   -2.94{col 78}{space 3}0.003{col 86}{space 4}-.1591342{col 99}{space 3}-.0318472
{txt}{space 37}girona  {c |}{col 46}{res}{space 2}-.0721269{col 58}{space 2} .0369294{col 69}{space 1}   -1.95{col 78}{space 3}0.051{col 86}{space 4}-.1445093{col 99}{space 3} .0002555
{txt}{space 36}granada  {c |}{col 46}{res}{space 2} .0284814{col 58}{space 2} .0280516{col 69}{space 1}    1.02{col 78}{space 3}0.310{col 86}{space 4}-.0265004{col 99}{space 3} .0834632
{txt}{space 32}guadalajara  {c |}{col 46}{res}{space 2} .1086307{col 58}{space 2} .0412678{col 69}{space 1}    2.63{col 78}{space 3}0.008{col 86}{space 4}  .027745{col 99}{space 3} .1895165
{txt}{space 37}huelva  {c |}{col 46}{res}{space 2}  .047247{col 58}{space 2} .0399919{col 69}{space 1}    1.18{col 78}{space 3}0.237{col 86}{space 4}-.0311379{col 99}{space 3}  .125632
{txt}{space 37}huesca  {c |}{col 46}{res}{space 2} .0353293{col 58}{space 2} .0455743{col 69}{space 1}    0.78{col 78}{space 3}0.438{col 86}{space 4}-.0539973{col 99}{space 3}  .124656
{txt}{space 29}islas baleares  {c |}{col 46}{res}{space 2} .0394102{col 58}{space 2} .0348355{col 69}{space 1}    1.13{col 78}{space 3}0.258{col 86}{space 4}-.0288681{col 99}{space 3} .1076885
{txt}{space 39}jaen  {c |}{col 46}{res}{space 2} .0631391{col 58}{space 2} .0317503{col 69}{space 1}    1.99{col 78}{space 3}0.047{col 86}{space 4} .0009078{col 99}{space 3} .1253704
{txt}{space 35}la rioja  {c |}{col 46}{res}{space 2} .0704293{col 58}{space 2} .0326446{col 69}{space 1}    2.16{col 78}{space 3}0.031{col 86}{space 4} .0064451{col 99}{space 3} .1344136
{txt}{space 33}las palmas  {c |}{col 46}{res}{space 2} .0169138{col 58}{space 2} .0318059{col 69}{space 1}    0.53{col 78}{space 3}0.595{col 86}{space 4}-.0454265{col 99}{space 3}  .079254
{txt}{space 39}leon  {c |}{col 46}{res}{space 2} .0700725{col 58}{space 2} .0380028{col 69}{space 1}    1.84{col 78}{space 3}0.065{col 86}{space 4}-.0044139{col 99}{space 3} .1445589
{txt}{space 37}lleida  {c |}{col 46}{res}{space 2} -.014935{col 58}{space 2} .0351715{col 69}{space 1}   -0.42{col 78}{space 3}0.671{col 86}{space 4} -.083872{col 99}{space 3}  .054002
{txt}{space 39}lugo  {c |}{col 46}{res}{space 2} .0074123{col 58}{space 2} .0375333{col 69}{space 1}    0.20{col 78}{space 3}0.843{col 86}{space 4}-.0661537{col 99}{space 3} .0809783
{txt}{space 37}madrid  {c |}{col 46}{res}{space 2} .0264959{col 58}{space 2} .0234548{col 69}{space 1}    1.13{col 78}{space 3}0.259{col 86}{space 4}-.0194761{col 99}{space 3} .0724678
{txt}{space 37}malaga  {c |}{col 46}{res}{space 2} .0275965{col 58}{space 2} .0252653{col 69}{space 1}    1.09{col 78}{space 3}0.275{col 86}{space 4}-.0219241{col 99}{space 3} .0771171
{txt}{space 37}murcia  {c |}{col 46}{res}{space 2} .0098175{col 58}{space 2} .0256626{col 69}{space 1}    0.38{col 78}{space 3}0.702{col 86}{space 4}-.0404818{col 99}{space 3} .0601168
{txt}{space 36}navarra  {c |}{col 46}{res}{space 2}-.0183907{col 58}{space 2} .0317992{col 69}{space 1}   -0.58{col 78}{space 3}0.563{col 86}{space 4}-.0807179{col 99}{space 3} .0439365
{txt}{space 36}ourense  {c |}{col 46}{res}{space 2} .0427679{col 58}{space 2} .0422543{col 69}{space 1}    1.01{col 78}{space 3}0.311{col 86}{space 4}-.0400514{col 99}{space 3} .1255872
{txt}{space 35}palencia  {c |}{col 46}{res}{space 2} .0381325{col 58}{space 2} .0544065{col 69}{space 1}    0.70{col 78}{space 3}0.483{col 86}{space 4}-.0685054{col 99}{space 3} .1447704
{txt}{space 33}pontevedra  {c |}{col 46}{res}{space 2} .0238938{col 58}{space 2} .0276813{col 69}{space 1}    0.86{col 78}{space 3}0.388{col 86}{space 4}-.0303623{col 99}{space 3} .0781498
{txt}{space 34}salamanca  {c |}{col 46}{res}{space 2} .0742022{col 58}{space 2} .0377317{col 69}{space 1}    1.97{col 78}{space 3}0.049{col 86}{space 4} .0002471{col 99}{space 3} .1481572
{txt}{space 21}santa cruz de tenerife  {c |}{col 46}{res}{space 2} .1042387{col 58}{space 2} .0271706{col 69}{space 1}    3.84{col 78}{space 3}0.000{col 86}{space 4} .0509838{col 99}{space 3} .1574937
{txt}{space 36}segovia  {c |}{col 46}{res}{space 2} .0348499{col 58}{space 2} .0614798{col 69}{space 1}    0.57{col 78}{space 3}0.571{col 86}{space 4}-.0856519{col 99}{space 3} .1553516
{txt}{space 36}sevilla  {c |}{col 46}{res}{space 2} .0198045{col 58}{space 2} .0269277{col 69}{space 1}    0.74{col 78}{space 3}0.462{col 86}{space 4}-.0329743{col 99}{space 3} .0725834
{txt}{space 38}soria  {c |}{col 46}{res}{space 2} .1802972{col 58}{space 2} .1029093{col 69}{space 1}    1.75{col 78}{space 3}0.080{col 86}{space 4}-.0214072{col 99}{space 3} .3820017
{txt}{space 34}tarragona  {c |}{col 46}{res}{space 2} -.035032{col 58}{space 2} .0359521{col 69}{space 1}   -0.97{col 78}{space 3}0.330{col 86}{space 4}-.1054989{col 99}{space 3} .0354349
{txt}{space 37}teruel  {c |}{col 46}{res}{space 2} .0938487{col 58}{space 2} .0581879{col 69}{space 1}    1.61{col 78}{space 3}0.107{col 86}{space 4}-.0202008{col 99}{space 3} .2078981
{txt}{space 37}toledo  {c |}{col 46}{res}{space 2} .0313145{col 58}{space 2}   .03206{col 69}{space 1}    0.98{col 78}{space 3}0.329{col 86}{space 4}-.0315238{col 99}{space 3} .0941529
{txt}{space 35}valencia  {c |}{col 46}{res}{space 2} .0470735{col 58}{space 2} .0227167{col 69}{space 1}    2.07{col 78}{space 3}0.038{col 86}{space 4} .0025482{col 99}{space 3} .0915988
{txt}{space 33}valladolid  {c |}{col 46}{res}{space 2} .0202323{col 58}{space 2} .0275909{col 69}{space 1}    0.73{col 78}{space 3}0.463{col 86}{space 4}-.0338465{col 99}{space 3} .0743112
{txt}{space 36}vizcaya  {c |}{col 46}{res}{space 2}-.0629977{col 58}{space 2} .0243079{col 69}{space 1}   -2.59{col 78}{space 3}0.010{col 86}{space 4}-.1106418{col 99}{space 3}-.0153536
{txt}{space 37}zamora  {c |}{col 46}{res}{space 2}  .028105{col 58}{space 2} .0526482{col 69}{space 1}    0.53{col 78}{space 3}0.593{col 86}{space 4}-.0750866{col 99}{space 3} .1312967
{txt}{space 35}zaragoza  {c |}{col 46}{res}{space 2} .0227479{col 58}{space 2} .0277127{col 69}{space 1}    0.82{col 78}{space 3}0.412{col 86}{space 4}-.0315697{col 99}{space 3} .0770654
{txt}{space 44} {c |}
{space 35}preperiod {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 27}municipality_size {c |}
{space 7}Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2}-.0035054{col 58}{space 2} .0161752{col 69}{space 1}   -0.22{col 78}{space 3}0.828{col 86}{space 4}-.0352092{col 99}{space 3} .0281984
{txt}{space 6}Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2}-.0006329{col 58}{space 2} .0153152{col 69}{space 1}   -0.04{col 78}{space 3}0.967{col 86}{space 4}-.0306509{col 99}{space 3} .0293852
{txt}{space 5}Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2} .0145104{col 58}{space 2}   .01622{col 69}{space 1}    0.89{col 78}{space 3}0.371{col 86}{space 4}-.0172811{col 99}{space 3} .0463018
{txt}{space 4}Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2}  .011563{col 58}{space 2} .0152167{col 69}{space 1}    0.76{col 78}{space 3}0.447{col 86}{space 4}-.0182621{col 99}{space 3} .0413882
{txt}{space 2}Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0099899{col 58}{space 2} .0191814{col 69}{space 1}   -0.52{col 78}{space 3}0.602{col 86}{space 4}-.0475859{col 99}{space 3}  .027606
{txt}{space 12}More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}-.0016157{col 58}{space 2} .0177101{col 69}{space 1}   -0.09{col 78}{space 3}0.927{col 86}{space 4}-.0363278{col 99}{space 3} .0330964
{txt}{space 44} {c |}
{space 38}female {c |}{col 46}{res}{space 2}-.0121593{col 58}{space 2} .0058337{col 69}{space 1}   -2.08{col 78}{space 3}0.037{col 86}{space 4}-.0235935{col 99}{space 3} -.000725
{txt}{space 41}age {c |}{col 46}{res}{space 2} .0016584{col 58}{space 2} .0002727{col 69}{space 1}    6.08{col 78}{space 3}0.000{col 86}{space 4} .0011239{col 99}{space 3} .0021928
{txt}{space 44} {c |}
{space 38}status {c |}
{space 33}Unemployed  {c |}{col 46}{res}{space 2} .0059291{col 58}{space 2} .0094592{col 69}{space 1}    0.63{col 78}{space 3}0.531{col 86}{space 4}-.0126112{col 99}{space 3} .0244694
{txt}{space 36}Retired  {c |}{col 46}{res}{space 2} .0333335{col 58}{space 2} .0103006{col 69}{space 1}    3.24{col 78}{space 3}0.001{col 86}{space 4}  .013144{col 99}{space 3}  .053523
{txt}{space 36}Student  {c |}{col 46}{res}{space 2} .0435588{col 58}{space 2} .0164779{col 69}{space 1}    2.64{col 78}{space 3}0.008{col 86}{space 4} .0112618{col 99}{space 3} .0758558
{txt}{space 32}Housekeeper  {c |}{col 46}{res}{space 2}-.0076312{col 58}{space 2} .0234806{col 69}{space 1}   -0.32{col 78}{space 3}0.745{col 86}{space 4}-.0536537{col 99}{space 3} .0383913
{txt}{space 44} {c |}
{space 17}preperiod#municipality_size {c |}
{space 14}1#Less than 2,000 inhabitants  {c |}{col 46}{res}{space 2}-.0263741{col 58}{space 2} .0253799{col 69}{space 1}   -1.04{col 78}{space 3}0.299{col 86}{space 4}-.0761193{col 99}{space 3} .0233711
{txt}{space 5}1#Between 2,001 and 10,000 inhabitants  {c |}{col 46}{res}{space 2}-.0357803{col 58}{space 2} .0201982{col 69}{space 1}   -1.77{col 78}{space 3}0.076{col 86}{space 4}-.0753692{col 99}{space 3} .0038087
{txt}{space 4}1#Between 10,001 and 50,000 inhabitants  {c |}{col 46}{res}{space 2}-.0428516{col 58}{space 2} .0181043{col 69}{space 1}   -2.37{col 78}{space 3}0.018{col 86}{space 4}-.0783365{col 99}{space 3}-.0073668
{txt}{space 3}1#Between 50,001 and 100,000 inhabitants  {c |}{col 46}{res}{space 2}-.0497701{col 58}{space 2}  .019658{col 69}{space 1}   -2.53{col 78}{space 3}0.011{col 86}{space 4}-.0883002{col 99}{space 3}-.0112401
{txt}{space 2}1#Between 100,001 and 400,000 inhabitants  {c |}{col 46}{res}{space 2}-.0432601{col 58}{space 2} .0179933{col 69}{space 1}   -2.40{col 78}{space 3}0.016{col 86}{space 4}-.0785273{col 99}{space 3}-.0079929
{txt}1#Between 400,001 and 1,000,000 inhabitants  {c |}{col 46}{res}{space 2}-.0185791{col 58}{space 2}  .026647{col 69}{space 1}   -0.70{col 78}{space 3}0.486{col 86}{space 4}-.0708077{col 99}{space 3} .0336496
{txt}{space 10}1#More than 1,000,001 inhabitants  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 28}preperiod#female {c |}
{space 40}0 1  {c |}{col 46}{res}{space 2} .0372498{col 58}{space 2} .0080661{col 69}{space 1}    4.62{col 78}{space 3}0.000{col 86}{space 4}   .02144{col 99}{space 3} .0530597
{txt}{space 40}1 0  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 40}1 1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 29}preperiod#c.age {c |}
{space 42}1  {c |}{col 46}{res}{space 2}-.0007652{col 58}{space 2} .0003958{col 69}{space 1}   -1.93{col 78}{space 3}0.053{col 86}{space 4}-.0015409{col 99}{space 3} .0000105
{txt}{space 44} {c |}
{space 28}preperiod#status {c |}
{space 33}1#Employed  {c |}{col 46}{res}{space 2}-.0346047{col 58}{space 2} .0276111{col 69}{space 1}   -1.25{col 78}{space 3}0.210{col 86}{space 4} -.088723{col 99}{space 3} .0195136
{txt}{space 31}1#Unemployed  {c |}{col 46}{res}{space 2}-.0702192{col 58}{space 2}   .02999{col 69}{space 1}   -2.34{col 78}{space 3}0.019{col 86}{space 4}-.1290001{col 99}{space 3}-.0114382
{txt}{space 34}1#Retired  {c |}{col 46}{res}{space 2}-.0443536{col 58}{space 2} .0304961{col 69}{space 1}   -1.45{col 78}{space 3}0.146{col 86}{space 4}-.1041266{col 99}{space 3} .0154194
{txt}{space 34}1#Student  {c |}{col 46}{res}{space 2}-.0947075{col 58}{space 2} .0334518{col 69}{space 1}   -2.83{col 78}{space 3}0.005{col 86}{space 4}-.1602738{col 99}{space 3}-.0291412
{txt}{space 30}1#Housekeeper  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 26}prov_num#preperiod {c |}
{space 33}a coruna#1  {c |}{col 46}{res}{space 2}-.0033996{col 58}{space 2} .0406085{col 69}{space 1}   -0.08{col 78}{space 3}0.933{col 86}{space 4}-.0829932{col 99}{space 3}  .076194
{txt}{space 36}alava#1  {c |}{col 46}{res}{space 2}-.0737789{col 58}{space 2} .0520863{col 69}{space 1}   -1.42{col 78}{space 3}0.157{col 86}{space 4}-.1758692{col 99}{space 3} .0283113
{txt}{space 33}albacete#1  {c |}{col 46}{res}{space 2}-.0304688{col 58}{space 2} .0541575{col 69}{space 1}   -0.56{col 78}{space 3}0.574{col 86}{space 4}-.1366188{col 99}{space 3} .0756811
{txt}{space 33}alicante#1  {c |}{col 46}{res}{space 2} .0434138{col 58}{space 2} .0427445{col 69}{space 1}    1.02{col 78}{space 3}0.310{col 86}{space 4}-.0403664{col 99}{space 3}  .127194
{txt}{space 34}almeria#1  {c |}{col 46}{res}{space 2} .0471129{col 58}{space 2} .0484751{col 69}{space 1}    0.97{col 78}{space 3}0.331{col 86}{space 4}-.0478993{col 99}{space 3} .1421251
{txt}{space 33}asturias#1  {c |}{col 46}{res}{space 2} .0515018{col 58}{space 2} .0455718{col 69}{space 1}    1.13{col 78}{space 3}0.258{col 86}{space 4}-.0378199{col 99}{space 3} .1408234
{txt}{space 36}avila#1  {c |}{col 46}{res}{space 2}  .067395{col 58}{space 2} .0665682{col 69}{space 1}    1.01{col 78}{space 3}0.311{col 86}{space 4}-.0630801{col 99}{space 3} .1978702
{txt}{space 34}badajoz#1  {c |}{col 46}{res}{space 2} .1413934{col 58}{space 2} .0485403{col 69}{space 1}    2.91{col 78}{space 3}0.004{col 86}{space 4} .0462534{col 99}{space 3} .2365334
{txt}{space 32}barcelona#1  {c |}{col 46}{res}{space 2} -.051506{col 58}{space 2} .0392103{col 69}{space 1}   -1.31{col 78}{space 3}0.189{col 86}{space 4}-.1283592{col 99}{space 3} .0253471
{txt}{space 35}burgos#1  {c |}{col 46}{res}{space 2}-.0238133{col 58}{space 2} .0560036{col 69}{space 1}   -0.43{col 78}{space 3}0.671{col 86}{space 4}-.1335815{col 99}{space 3} .0859549
{txt}{space 34}caceres#1  {c |}{col 46}{res}{space 2}-.0704325{col 58}{space 2} .0538765{col 69}{space 1}   -1.31{col 78}{space 3}0.191{col 86}{space 4}-.1760317{col 99}{space 3} .0351667
{txt}{space 36}cadiz#1  {c |}{col 46}{res}{space 2} .0025722{col 58}{space 2} .0471517{col 69}{space 1}    0.05{col 78}{space 3}0.956{col 86}{space 4}-.0898461{col 99}{space 3} .0949905
{txt}{space 32}cantabria#1  {c |}{col 46}{res}{space 2} .0303329{col 58}{space 2} .0447378{col 69}{space 1}    0.68{col 78}{space 3}0.498{col 86}{space 4}-.0573542{col 99}{space 3} .1180199
{txt}{space 32}castellon#1  {c |}{col 46}{res}{space 2} .0855366{col 58}{space 2} .0478965{col 69}{space 1}    1.79{col 78}{space 3}0.074{col 86}{space 4}-.0083416{col 99}{space 3} .1794149
{txt}{space 30}ciudad real#1  {c |}{col 46}{res}{space 2}  .083756{col 58}{space 2} .0539313{col 69}{space 1}    1.55{col 78}{space 3}0.120{col 86}{space 4}-.0219505{col 99}{space 3} .1894626
{txt}{space 34}cordoba#1  {c |}{col 46}{res}{space 2} .0312962{col 58}{space 2} .0458498{col 69}{space 1}    0.68{col 78}{space 3}0.495{col 86}{space 4}-.0585704{col 99}{space 3} .1211628
{txt}{space 35}cuenca#1  {c |}{col 46}{res}{space 2} .1755505{col 58}{space 2} .0694588{col 69}{space 1}    2.53{col 78}{space 3}0.011{col 86}{space 4} .0394098{col 99}{space 3} .3116913
{txt}{space 33}gipuzkoa#1  {c |}{col 46}{res}{space 2}-.0797208{col 58}{space 2}  .044725{col 69}{space 1}   -1.78{col 78}{space 3}0.075{col 86}{space 4}-.1673827{col 99}{space 3} .0079411
{txt}{space 35}girona#1  {c |}{col 46}{res}{space 2}-.0253234{col 58}{space 2} .0508672{col 69}{space 1}   -0.50{col 78}{space 3}0.619{col 86}{space 4}-.1250243{col 99}{space 3} .0743775
{txt}{space 34}granada#1  {c |}{col 46}{res}{space 2} .0392831{col 58}{space 2}  .044202{col 69}{space 1}    0.89{col 78}{space 3}0.374{col 86}{space 4}-.0473538{col 99}{space 3} .1259199
{txt}{space 30}guadalajara#1  {c |}{col 46}{res}{space 2} .0139016{col 58}{space 2} .0569021{col 69}{space 1}    0.24{col 78}{space 3}0.807{col 86}{space 4}-.0976277{col 99}{space 3} .1254308
{txt}{space 35}huelva#1  {c |}{col 46}{res}{space 2}  -.02086{col 58}{space 2} .0572078{col 69}{space 1}   -0.36{col 78}{space 3}0.715{col 86}{space 4}-.1329885{col 99}{space 3} .0912685
{txt}{space 35}huesca#1  {c |}{col 46}{res}{space 2} .0136297{col 58}{space 2} .0610484{col 69}{space 1}    0.22{col 78}{space 3}0.823{col 86}{space 4}-.1060264{col 99}{space 3} .1332858
{txt}{space 27}islas baleares#1  {c |}{col 46}{res}{space 2} .0174923{col 58}{space 2} .0476555{col 69}{space 1}    0.37{col 78}{space 3}0.714{col 86}{space 4}-.0759135{col 99}{space 3}  .110898
{txt}{space 37}jaen#1  {c |}{col 46}{res}{space 2} .0016189{col 58}{space 2} .0479689{col 69}{space 1}    0.03{col 78}{space 3}0.973{col 86}{space 4}-.0924013{col 99}{space 3}  .095639
{txt}{space 33}la rioja#1  {c |}{col 46}{res}{space 2}-.0246488{col 58}{space 2} .0500036{col 69}{space 1}   -0.49{col 78}{space 3}0.622{col 86}{space 4}-.1226569{col 99}{space 3} .0733593
{txt}{space 31}las palmas#1  {c |}{col 46}{res}{space 2}  .024159{col 58}{space 2} .0477694{col 69}{space 1}    0.51{col 78}{space 3}0.613{col 86}{space 4}-.0694701{col 99}{space 3} .1177881
{txt}{space 37}leon#1  {c |}{col 46}{res}{space 2}  .033508{col 58}{space 2} .0539192{col 69}{space 1}    0.62{col 78}{space 3}0.534{col 86}{space 4}-.0721748{col 99}{space 3} .1391909
{txt}{space 35}lleida#1  {c |}{col 46}{res}{space 2}-.0747276{col 58}{space 2} .0558123{col 69}{space 1}   -1.34{col 78}{space 3}0.181{col 86}{space 4}-.1841211{col 99}{space 3} .0346658
{txt}{space 37}lugo#1  {c |}{col 46}{res}{space 2} .0394129{col 58}{space 2} .0531171{col 69}{space 1}    0.74{col 78}{space 3}0.458{col 86}{space 4}-.0646979{col 99}{space 3} .1435236
{txt}{space 35}madrid#1  {c |}{col 46}{res}{space 2} .0044594{col 58}{space 2} .0364182{col 69}{space 1}    0.12{col 78}{space 3}0.903{col 86}{space 4}-.0669211{col 99}{space 3} .0758398
{txt}{space 35}malaga#1  {c |}{col 46}{res}{space 2}  .058825{col 58}{space 2} .0365822{col 69}{space 1}    1.61{col 78}{space 3}0.108{col 86}{space 4}-.0128768{col 99}{space 3} .1305268
{txt}{space 35}murcia#1  {c |}{col 46}{res}{space 2} .0183765{col 58}{space 2} .0391176{col 69}{space 1}    0.47{col 78}{space 3}0.639{col 86}{space 4}-.0582949{col 99}{space 3} .0950478
{txt}{space 34}navarra#1  {c |}{col 46}{res}{space 2}-.0436455{col 58}{space 2} .0463658{col 69}{space 1}   -0.94{col 78}{space 3}0.347{col 86}{space 4}-.1345234{col 99}{space 3} .0472324
{txt}{space 34}ourense#1  {c |}{col 46}{res}{space 2}-.0056819{col 58}{space 2} .0564824{col 69}{space 1}   -0.10{col 78}{space 3}0.920{col 86}{space 4}-.1163886{col 99}{space 3} .1050249
{txt}{space 33}palencia#1  {c |}{col 46}{res}{space 2} .0416148{col 58}{space 2} .0733248{col 69}{space 1}    0.57{col 78}{space 3}0.570{col 86}{space 4}-.1021034{col 99}{space 3} .1853331
{txt}{space 31}pontevedra#1  {c |}{col 46}{res}{space 2}-.0018066{col 58}{space 2} .0426369{col 69}{space 1}   -0.04{col 78}{space 3}0.966{col 86}{space 4}-.0853758{col 99}{space 3} .0817627
{txt}{space 32}salamanca#1  {c |}{col 46}{res}{space 2} .0223296{col 58}{space 2} .0528675{col 69}{space 1}    0.42{col 78}{space 3}0.673{col 86}{space 4}-.0812918{col 99}{space 3}  .125951
{txt}{space 19}santa cruz de tenerife#1  {c |}{col 46}{res}{space 2}-.0609101{col 58}{space 2} .0433417{col 69}{space 1}   -1.41{col 78}{space 3}0.160{col 86}{space 4}-.1458607{col 99}{space 3} .0240405
{txt}{space 34}segovia#1  {c |}{col 46}{res}{space 2}-.0714013{col 58}{space 2} .0754903{col 69}{space 1}   -0.95{col 78}{space 3}0.344{col 86}{space 4}-.2193639{col 99}{space 3} .0765614
{txt}{space 34}sevilla#1  {c |}{col 46}{res}{space 2} .0224678{col 58}{space 2} .0363593{col 69}{space 1}    0.62{col 78}{space 3}0.537{col 86}{space 4}-.0487971{col 99}{space 3} .0937328
{txt}{space 36}soria#1  {c |}{col 46}{res}{space 2}-.1300334{col 58}{space 2} .1156215{col 69}{space 1}   -1.12{col 78}{space 3}0.261{col 86}{space 4}-.3566542{col 99}{space 3} .0965873
{txt}{space 32}tarragona#1  {c |}{col 46}{res}{space 2} .0198045{col 58}{space 2}  .052063{col 69}{space 1}    0.38{col 78}{space 3}0.704{col 86}{space 4}-.0822401{col 99}{space 3} .1218492
{txt}{space 35}teruel#1  {c |}{col 46}{res}{space 2}-.1384516{col 58}{space 2} .0722415{col 69}{space 1}   -1.92{col 78}{space 3}0.055{col 86}{space 4}-.2800464{col 99}{space 3} .0031433
{txt}{space 35}toledo#1  {c |}{col 46}{res}{space 2} .0971138{col 58}{space 2} .0499343{col 69}{space 1}    1.94{col 78}{space 3}0.052{col 86}{space 4}-.0007586{col 99}{space 3} .1949863
{txt}{space 33}valencia#1  {c |}{col 46}{res}{space 2} .0144238{col 58}{space 2} .0328758{col 69}{space 1}    0.44{col 78}{space 3}0.661{col 86}{space 4}-.0500136{col 99}{space 3} .0788611
{txt}{space 31}valladolid#1  {c |}{col 46}{res}{space 2} .0127956{col 58}{space 2} .0443643{col 69}{space 1}    0.29{col 78}{space 3}0.773{col 86}{space 4}-.0741594{col 99}{space 3} .0997506
{txt}{space 34}vizcaya#1  {c |}{col 46}{res}{space 2}-.0734785{col 58}{space 2} .0380764{col 69}{space 1}   -1.93{col 78}{space 3}0.054{col 86}{space 4}-.1481092{col 99}{space 3} .0011521
{txt}{space 35}zamora#1  {c |}{col 46}{res}{space 2} .1444709{col 58}{space 2} .0690248{col 69}{space 1}    2.09{col 78}{space 3}0.036{col 86}{space 4} .0091808{col 99}{space 3} .2797611
{txt}{space 33}zaragoza#1  {c |}{col 46}{res}{space 2}        0{col 58}{txt}  (omitted)
{space 44} {c |}
{space 39}_cons {c |}{col 46}{res}{space 2} .1789064{col 58}{space 2} .0433083{col 69}{space 1}    4.13{col 78}{space 3}0.000{col 86}{space 4} .0940212{col 99}{space 3} .2637917
{txt}{hline 45}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
F test of absorbed indicators: F({res}55{txt}, {res}40950{txt}) = {res}1.794{col 62}{txt} Prob > F = {res}0.000

{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(ProvinceFE) : "{res:$\checkmark$}"

added macro:
             e(YearFE) : "{res:$\checkmark$}"

added macro:
           e(PeriodFE) : "{res:$\checkmark$}"

added macro:
       e(PeriodCtrols) : "{res:$\checkmark$}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
             e(Sample) : "{res:Pooled}"

added macro:
         e(Propensity) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
            e(Outcome) : "{res:Q1}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    1.5845
{col 25}{txt}Prob>|t| = {res}    0.1441

95%{txt} confidence set for null hypothesis expression: {res}[−.005015, .03622]

{txt}{col 1}Mean estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 6:41,165}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 16}{c |}       Mean{col 28}   Std. err.{col 40}     [95% con{col 53}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
vote_incumbent {c |}{col 16}{res}{space 2} .2080651{col 28}{space 2} .0020007{col 39}{space 5} .2041437{col 53}{space 3} .2119866
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Tables_24_25_26_higher.tex"'})

{com}. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLE 27: Survey Results Using Only the Last Year's Lottery Prize, 
. *** by Year and Estimation Type (OLS by year, Meta-Analysis for pooled results), 
. *** Using Our Data
. **----------------------------------------------------------------------------**
. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. 
. ** Set of controls:
. global individual_characteristics "i.municipality_size female age i.education i.status"
{txt}
{com}. global ind_char_preperiod "preperiod#i.municipality_size preperiod#female preperiod#c.age preperiod#i.education preperiod#i.status"
{txt}
{com}. 
. ** Models only for Q1:
. keep if month<4
{txt}(783,192 observations deleted)

{com}. 
. 
. * Step 1: Initialize and Loop through All Years
. 
. local regression_names ""
{txt}
{com}. 
. * Initialize the matrix with 36 rows (1987-2022) and 6 columns (coefficients, lower bounds, upper bounds)
. matrix bootstrap_results = J(36, 3, .) 
{txt}
{com}. 
. forvalues y = 1987/2022 {c -(}
{txt}  2{com}.     * Run the regression for the current year
.     eststo boots_Q1_`y': reg vote_incumbent top_prizes_gdp_ours expenditure_gdp $individual_characteristics i.survey if year1 ==`y', cluster(prov_num)
{txt}  3{com}.                 
.         estadd local SurveyFE "$\checkmark$"
{txt}  4{com}.         estadd local Estimation "OLS"
{txt}  5{com}.         estadd local Data "Ours"
{txt}  6{com}.         estadd local Outcome "Q1"
{txt}  7{com}.         estadd local ProvinceFE "$\times$"
{txt}  8{com}.         estadd local Year "`y'"
{txt}  9{com}.         
.         sum vote_incumbent if e(sample)==1
{txt} 10{com}.         
.         
.     * Run boottest for top_prizes_gdp
.         boottest top_prizes_gdp_ours, cluster(prov_num) seed(12345)
{txt} 11{com}.         
.         
.     * Capture confidence set bounds from the r(CI) matrix
.         matrix top_prizes_CI = r(CI)
{txt} 12{com}.     local top_prizes_ci_lower = top_prizes_CI[1,1]
{txt} 13{com}.     local top_prizes_ci_upper = top_prizes_CI[1,2]
{txt} 14{com}.                     
.     
.     * Calculate the row number based on the year
.     local row = `y' - 1986  // 1987 starts at row 1, 1988 at row 2, etc.
{txt} 15{com}.     
.     * Store the coefficients and both confidence interval bounds in the matrix
.     matrix bootstrap_results[`row', 1] = _b[top_prizes_gdp_ours]
{txt} 16{com}.         matrix bootstrap_results[`row', 2] = `top_prizes_ci_lower'
{txt} 17{com}.     matrix bootstrap_results[`row', 3] = `top_prizes_ci_upper'
{txt} 18{com}. 
.             
.     * Add regression name to the list
.     local regression_names "`regression_names' boots_Q1_`y'"
{txt} 19{com}.         
.         dis as error `y'
{txt} 20{com}.         
.         tab survey if e(sample)==1
{txt} 21{com}.         
. {c )-}
{txt}{p 0 6 2}note: {bf:1595.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,451
                                                {txt}F(16, 38)         =  {res}     6.23
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0296
                                                {txt}Root MSE          =    {res}  .4267

{txt}{ralign 108:(Std. err. adjusted for {res:39} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}  .037527{col 56}{space 2} .0525645{col 67}{space 1}    0.71{col 76}{space 3}0.480{col 84}{space 4}-.0688843{col 97}{space 3} .1439383
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.4504282{col 56}{space 2} .1973316{col 67}{space 1}   -2.28{col 76}{space 3}0.028{col 84}{space 4}-.8499052{col 97}{space 3}-.0509513
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0231463{col 56}{space 2}  .050102{col 67}{space 1}    0.46{col 76}{space 3}0.647{col 84}{space 4}-.0782798{col 97}{space 3} .1245725
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0130302{col 56}{space 2} .0399552{col 67}{space 1}    0.33{col 76}{space 3}0.746{col 84}{space 4}-.0678549{col 97}{space 3} .0939153
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0323398{col 56}{space 2} .0507378{col 67}{space 1}    0.64{col 76}{space 3}0.528{col 84}{space 4}-.0703735{col 97}{space 3}  .135053
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0236176{col 56}{space 2} .0465086{col 67}{space 1}    0.51{col 76}{space 3}0.615{col 84}{space 4}-.0705342{col 97}{space 3} .1177694
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}  .038266{col 56}{space 2} .0487111{col 67}{space 1}    0.79{col 76}{space 3}0.437{col 84}{space 4}-.0603444{col 97}{space 3} .1368763
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0742544{col 56}{space 2} .0436108{col 67}{space 1}    1.70{col 76}{space 3}0.097{col 84}{space 4}-.0140312{col 97}{space 3} .1625399
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0632571{col 56}{space 2}  .018893{col 67}{space 1}   -3.35{col 76}{space 3}0.002{col 84}{space 4} -.101504{col 97}{space 3}-.0250101
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0011607{col 56}{space 2} .0007923{col 67}{space 1}   -1.46{col 76}{space 3}0.151{col 84}{space 4}-.0027646{col 97}{space 3} .0004433
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0628457{col 56}{space 2} .0239046{col 67}{space 1}   -2.63{col 76}{space 3}0.012{col 84}{space 4} -.111238{col 97}{space 3}-.0144534
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1132663{col 56}{space 2} .0254168{col 67}{space 1}   -4.46{col 76}{space 3}0.000{col 84}{space 4}-.1647201{col 97}{space 3}-.0618126
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0106388{col 56}{space 2} .0372002{col 67}{space 1}    0.29{col 76}{space 3}0.776{col 84}{space 4} -.064669{col 97}{space 3} .0859466
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0077118{col 56}{space 2} .0253497{col 67}{space 1}   -0.30{col 76}{space 3}0.763{col 84}{space 4}-.0590296{col 97}{space 3} .0436061
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.1574536{col 56}{space 2} .0288267{col 67}{space 1}   -5.46{col 76}{space 3}0.000{col 84}{space 4}-.2158102{col 97}{space 3} -.099097
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0129803{col 56}{space 2} .0207982{col 67}{space 1}   -0.62{col 76}{space 3}0.536{col 84}{space 4}-.0550841{col 97}{space 3} .0291235
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1595  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .4564045{col 56}{space 2} .0764509{col 67}{space 1}    5.97{col 76}{space 3}0.000{col 84}{space 4} .3016378{col 97}{space 3} .6111712
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1987}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,451     .247654    .4317378          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(38) = {res}    0.7139
{col 25}{txt}Prob>|t| = {res}    0.5485

95%{txt} confidence set for null hypothesis expression: {res}[−1.042, .3596]
{err}1987

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1595 {c |}{res}      2,451      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,451      100.00
{txt}{p 0 6 2}note: {bf:1725.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,424
                                                {txt}F(16, 44)         =  {res}     7.06
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0390
                                                {txt}Root MSE          =    {res}  .4636

{txt}{ralign 108:(Std. err. adjusted for {res:45} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0375374{col 56}{space 2} .0180553{col 67}{space 1}    2.08{col 76}{space 3}0.043{col 84}{space 4} .0011492{col 97}{space 3} .0739255
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} -.612145{col 56}{space 2} .3352087{col 67}{space 1}   -1.83{col 76}{space 3}0.075{col 84}{space 4}-1.287714{col 97}{space 3} .0634237
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .1049863{col 56}{space 2} .0518261{col 67}{space 1}    2.03{col 76}{space 3}0.049{col 84}{space 4} .0005376{col 97}{space 3}  .209435
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .1545023{col 56}{space 2} .0505613{col 67}{space 1}    3.06{col 76}{space 3}0.004{col 84}{space 4} .0526026{col 97}{space 3} .2564019
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .1419669{col 56}{space 2} .0663864{col 67}{space 1}    2.14{col 76}{space 3}0.038{col 84}{space 4}  .008174{col 97}{space 3} .2757599
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0648912{col 56}{space 2} .0503211{col 67}{space 1}    1.29{col 76}{space 3}0.204{col 84}{space 4}-.0365243{col 97}{space 3} .1663068
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0850053{col 56}{space 2} .0504742{col 67}{space 1}    1.68{col 76}{space 3}0.099{col 84}{space 4}-.0167188{col 97}{space 3} .1867294
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}  .194574{col 56}{space 2} .0498378{col 67}{space 1}    3.90{col 76}{space 3}0.000{col 84}{space 4} .0941324{col 97}{space 3} .2950156
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0373123{col 56}{space 2} .0171744{col 67}{space 1}   -2.17{col 76}{space 3}0.035{col 84}{space 4} -.071925{col 97}{space 3}-.0026997
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0021445{col 56}{space 2} .0008776{col 67}{space 1}   -2.44{col 76}{space 3}0.019{col 84}{space 4}-.0039132{col 97}{space 3}-.0003757
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.1459212{col 56}{space 2} .0279176{col 67}{space 1}   -5.23{col 76}{space 3}0.000{col 84}{space 4}-.2021855{col 97}{space 3} -.089657
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1881902{col 56}{space 2} .0333436{col 67}{space 1}   -5.64{col 76}{space 3}0.000{col 84}{space 4}-.2553898{col 97}{space 3}-.1209906
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0069906{col 56}{space 2} .0359081{col 67}{space 1}    0.19{col 76}{space 3}0.847{col 84}{space 4}-.0653774{col 97}{space 3} .0793586
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0795267{col 56}{space 2} .0398068{col 67}{space 1}    2.00{col 76}{space 3}0.052{col 84}{space 4}-.0006986{col 97}{space 3}  .159752
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0607338{col 56}{space 2} .0452631{col 67}{space 1}   -1.34{col 76}{space 3}0.187{col 84}{space 4}-.1519556{col 97}{space 3}  .030488
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}  .009173{col 56}{space 2} .0359239{col 67}{space 1}    0.26{col 76}{space 3}0.800{col 84}{space 4}-.0632268{col 97}{space 3} .0815728
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1725  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .5162258{col 56}{space 2} .1102327{col 67}{space 1}    4.68{col 76}{space 3}0.000{col 84}{space 4} .2940664{col 97}{space 3} .7383852
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1988}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,424    .3329208    .4713557          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(44) = {res}    2.0790
{col 25}{txt}Prob>|t| = {res}    0.2833

95%{txt} confidence set for null hypothesis expression: {res}[−1.032, .2232]
{err}1988

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1725 {c |}{res}      2,424      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,424      100.00

{txt}Linear regression                               Number of obs     = {res}     7,288
                                                {txt}F(18, 49)         =  {res}     7.28
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0192
                                                {txt}Root MSE          =    {res} .43164

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.1105954{col 56}{space 2}  .044965{col 67}{space 1}   -2.46{col 76}{space 3}0.017{col 84}{space 4} -.200956{col 97}{space 3}-.0202348
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.2473421{col 56}{space 2}  .226111{col 67}{space 1}   -1.09{col 76}{space 3}0.279{col 84}{space 4}-.7017292{col 97}{space 3} .2070451
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0213965{col 56}{space 2} .0357063{col 67}{space 1}    0.60{col 76}{space 3}0.552{col 84}{space 4}-.0503579{col 97}{space 3} .0931509
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0435724{col 56}{space 2} .0362707{col 67}{space 1}    1.20{col 76}{space 3}0.235{col 84}{space 4}-.0293162{col 97}{space 3}  .116461
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0268552{col 56}{space 2} .0337554{col 67}{space 1}    0.80{col 76}{space 3}0.430{col 84}{space 4}-.0409788{col 97}{space 3} .0946892
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0343736{col 56}{space 2} .0316181{col 67}{space 1}    1.09{col 76}{space 3}0.282{col 84}{space 4}-.0291653{col 97}{space 3} .0979126
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0129685{col 56}{space 2} .0321852{col 67}{space 1}    0.40{col 76}{space 3}0.689{col 84}{space 4}-.0517101{col 97}{space 3} .0776471
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0088228{col 56}{space 2} .0337022{col 67}{space 1}    0.26{col 76}{space 3}0.795{col 84}{space 4}-.0589043{col 97}{space 3} .0765499
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0362037{col 56}{space 2}  .014384{col 67}{space 1}   -2.52{col 76}{space 3}0.015{col 84}{space 4}-.0651094{col 97}{space 3} -.007298
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0003801{col 56}{space 2}  .000432{col 67}{space 1}   -0.88{col 76}{space 3}0.383{col 84}{space 4}-.0012483{col 97}{space 3}  .000488
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0780113{col 56}{space 2} .0164929{col 67}{space 1}   -4.73{col 76}{space 3}0.000{col 84}{space 4} -.111155{col 97}{space 3}-.0448675
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1072285{col 56}{space 2} .0196942{col 67}{space 1}   -5.44{col 76}{space 3}0.000{col 84}{space 4}-.1468054{col 97}{space 3}-.0676516
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0562842{col 56}{space 2} .0183282{col 67}{space 1}    3.07{col 76}{space 3}0.003{col 84}{space 4} .0194523{col 97}{space 3} .0931161
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0513413{col 56}{space 2}  .023475{col 67}{space 1}    2.19{col 76}{space 3}0.034{col 84}{space 4} .0041665{col 97}{space 3} .0985162
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0190416{col 56}{space 2} .0203086{col 67}{space 1}    0.94{col 76}{space 3}0.353{col 84}{space 4}  -.02177{col 97}{space 3} .0598533
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0047741{col 56}{space 2} .0142579{col 67}{space 1}   -0.33{col 76}{space 3}0.739{col 84}{space 4}-.0334265{col 97}{space 3} .0238783
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1791  {c |}{col 44}{res}{space 2}-.0317211{col 56}{space 2} .0141932{col 67}{space 1}   -2.23{col 76}{space 3}0.030{col 84}{space 4}-.0602435{col 97}{space 3}-.0031988
{txt}{space 37}1798  {c |}{col 44}{res}{space 2} .0060382{col 56}{space 2} .0128911{col 67}{space 1}    0.47{col 76}{space 3}0.642{col 84}{space 4}-.0198674{col 97}{space 3} .0319439
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  .351585{col 56}{space 2}  .073565{col 67}{space 1}    4.78{col 76}{space 3}0.000{col 84}{space 4} .2037507{col 97}{space 3} .4994193
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1989}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,288    .2539791    .4353157          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -2.4596
{col 25}{txt}Prob>|t| = {res}    0.7648

95%{txt} confidence set for null hypothesis expression: {res}[−.875, .6573]
{err}1989

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1785 {c |}{res}      2,446       33.56       33.56
{txt}       1791 {c |}{res}      2,413       33.11       66.67
{txt}       1798 {c |}{res}      2,429       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,288      100.00

{txt}Linear regression                               Number of obs     = {res}     7,684
                                                {txt}F(18, 49)         =  {res}    19.46
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0333
                                                {txt}Root MSE          =    {res} .44476

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0423564{col 56}{space 2} .0311384{col 67}{space 1}    1.36{col 76}{space 3}0.180{col 84}{space 4}-.0202186{col 97}{space 3} .1049313
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} -.442819{col 56}{space 2} .3233388{col 67}{space 1}   -1.37{col 76}{space 3}0.177{col 84}{space 4}-1.092593{col 97}{space 3} .2069546
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0339786{col 56}{space 2} .0280188{col 67}{space 1}   -1.21{col 76}{space 3}0.231{col 84}{space 4}-.0902845{col 97}{space 3} .0223272
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0189112{col 56}{space 2} .0301221{col 67}{space 1}    0.63{col 76}{space 3}0.533{col 84}{space 4}-.0416215{col 97}{space 3} .0794439
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0146096{col 56}{space 2} .0355687{col 67}{space 1}   -0.41{col 76}{space 3}0.683{col 84}{space 4}-.0860877{col 97}{space 3} .0568684
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0040623{col 56}{space 2}  .031728{col 67}{space 1}    0.13{col 76}{space 3}0.899{col 84}{space 4}-.0596976{col 97}{space 3} .0678221
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} -.007867{col 56}{space 2} .0524675{col 67}{space 1}   -0.15{col 76}{space 3}0.881{col 84}{space 4}-.1133044{col 97}{space 3} .0975704
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0003971{col 56}{space 2} .0363147{col 67}{space 1}    0.01{col 76}{space 3}0.991{col 84}{space 4}  -.07258{col 97}{space 3} .0733742
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0266677{col 56}{space 2} .0171239{col 67}{space 1}   -1.56{col 76}{space 3}0.126{col 84}{space 4}-.0610794{col 97}{space 3}  .007744
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0018457{col 56}{space 2} .0004899{col 67}{space 1}   -3.77{col 76}{space 3}0.000{col 84}{space 4}-.0028303{col 97}{space 3}-.0008612
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.1278059{col 56}{space 2}  .018702{col 67}{space 1}   -6.83{col 76}{space 3}0.000{col 84}{space 4} -.165389{col 97}{space 3}-.0902228
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} -.161056{col 56}{space 2} .0233623{col 67}{space 1}   -6.89{col 76}{space 3}0.000{col 84}{space 4}-.2080043{col 97}{space 3}-.1141077
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0183238{col 56}{space 2} .0233706{col 67}{space 1}    0.78{col 76}{space 3}0.437{col 84}{space 4}-.0286411{col 97}{space 3} .0652887
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0821112{col 56}{space 2} .0234093{col 67}{space 1}    3.51{col 76}{space 3}0.001{col 84}{space 4} .0350684{col 97}{space 3} .1291539
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0724406{col 56}{space 2} .0258752{col 67}{space 1}   -2.80{col 76}{space 3}0.007{col 84}{space 4}-.1244388{col 97}{space 3}-.0204424
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0168743{col 56}{space 2} .0240649{col 67}{space 1}    0.70{col 76}{space 3}0.486{col 84}{space 4} -.031486{col 97}{space 3} .0652346
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1860  {c |}{col 44}{res}{space 2}-.0422817{col 56}{space 2} .0174517{col 67}{space 1}   -2.42{col 76}{space 3}0.019{col 84}{space 4}-.0773522{col 97}{space 3}-.0072112
{txt}{space 37}1864  {c |}{col 44}{res}{space 2}-.0529519{col 56}{space 2}   .01742{col 67}{space 1}   -3.04{col 76}{space 3}0.004{col 84}{space 4}-.0879587{col 97}{space 3}-.0179452
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .5535419{col 56}{space 2}  .089559{col 67}{space 1}    6.18{col 76}{space 3}0.000{col 84}{space 4} .3735663{col 97}{space 3} .7335176
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1990}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,684    .2857887    .4518186          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    1.3603
{col 25}{txt}Prob>|t| = {res}    0.2523

95%{txt} confidence set for null hypothesis expression: {res}[−.2098, .26]
{err}1990

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1858 {c |}{res}      2,844       37.01       37.01
{txt}       1860 {c |}{res}      2,410       31.36       68.38
{txt}       1864 {c |}{res}      2,430       31.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,684      100.00
{txt}{p 0 6 2}note: {bf:1913.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,382
                                                {txt}F(16, 44)         =  {res}     4.33
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0267
                                                {txt}Root MSE          =    {res} .44272

{txt}{ralign 108:(Std. err. adjusted for {res:45} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0683455{col 56}{space 2} .1640738{col 67}{space 1}   -0.42{col 76}{space 3}0.679{col 84}{space 4}-.3990145{col 97}{space 3} .2623235
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.3812222{col 56}{space 2} .3040265{col 67}{space 1}   -1.25{col 76}{space 3}0.216{col 84}{space 4}-.9939474{col 97}{space 3} .2315031
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0578393{col 56}{space 2} .0420438{col 67}{space 1}    1.38{col 76}{space 3}0.176{col 84}{space 4}-.0268945{col 97}{space 3} .1425732
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0290494{col 56}{space 2} .0509965{col 67}{space 1}    0.57{col 76}{space 3}0.572{col 84}{space 4}-.0737272{col 97}{space 3} .1318261
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}  .060912{col 56}{space 2}  .061426{col 67}{space 1}    0.99{col 76}{space 3}0.327{col 84}{space 4}-.0628841{col 97}{space 3}  .184708
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0316847{col 56}{space 2} .0524997{col 67}{space 1}    0.60{col 76}{space 3}0.549{col 84}{space 4}-.0741215{col 97}{space 3} .1374909
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0484666{col 56}{space 2} .0540379{col 67}{space 1}    0.90{col 76}{space 3}0.375{col 84}{space 4}-.0604396{col 97}{space 3} .1573728
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0155694{col 56}{space 2} .0574496{col 67}{space 1}    0.27{col 76}{space 3}0.788{col 84}{space 4}-.1002126{col 97}{space 3} .1313514
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0730984{col 56}{space 2} .0278893{col 67}{space 1}   -2.62{col 76}{space 3}0.012{col 84}{space 4}-.1293055{col 97}{space 3}-.0168914
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0013033{col 56}{space 2} .0007482{col 67}{space 1}   -1.74{col 76}{space 3}0.089{col 84}{space 4}-.0028112{col 97}{space 3} .0002047
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0900745{col 56}{space 2} .0245141{col 67}{space 1}   -3.67{col 76}{space 3}0.001{col 84}{space 4}-.1394795{col 97}{space 3}-.0406695
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1330749{col 56}{space 2}  .031809{col 67}{space 1}   -4.18{col 76}{space 3}0.000{col 84}{space 4}-.1971818{col 97}{space 3} -.068968
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0006379{col 56}{space 2} .0419252{col 67}{space 1}   -0.02{col 76}{space 3}0.988{col 84}{space 4}-.0851325{col 97}{space 3} .0838568
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0487071{col 56}{space 2} .0405562{col 67}{space 1}    1.20{col 76}{space 3}0.236{col 84}{space 4}-.0330285{col 97}{space 3} .1304427
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0007111{col 56}{space 2} .0427698{col 67}{space 1}   -0.02{col 76}{space 3}0.987{col 84}{space 4}-.0869079{col 97}{space 3} .0854857
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0736483{col 56}{space 2} .0234036{col 67}{space 1}    3.15{col 76}{space 3}0.003{col 84}{space 4} .0264815{col 97}{space 3} .1208152
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1913  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .4413095{col 56}{space 2}  .096467{col 67}{space 1}    4.57{col 76}{space 3}0.000{col 84}{space 4}  .246893{col 97}{space 3} .6357261
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1991}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,382    .2762385    .4472301          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(44) = {res}   -0.4166
{col 25}{txt}Prob>|t| = {res}    0.7287

95%{txt} confidence set for null hypothesis expression: {res}[−.4613, .5599]
{err}1991

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1913 {c |}{res}      2,382      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,382      100.00

{txt}Linear regression                               Number of obs     = {res}     4,761
                                                {txt}F(17, 44)         =  {res}    22.26
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0285
                                                {txt}Root MSE          =    {res} .44236

{txt}{ralign 108:(Std. err. adjusted for {res:45} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0147779{col 56}{space 2} .0700185{col 67}{space 1}    0.21{col 76}{space 3}0.834{col 84}{space 4}-.1263351{col 97}{space 3} .1558909
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.3454841{col 56}{space 2}  .187526{col 67}{space 1}   -1.84{col 76}{space 3}0.072{col 84}{space 4}-.7234179{col 97}{space 3} .0324496
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0200436{col 56}{space 2}  .035009{col 67}{space 1}   -0.57{col 76}{space 3}0.570{col 84}{space 4}-.0905996{col 97}{space 3} .0505124
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0274516{col 56}{space 2} .0426643{col 67}{space 1}    0.64{col 76}{space 3}0.523{col 84}{space 4}-.0585326{col 97}{space 3} .1134358
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}  .017483{col 56}{space 2}  .041062{col 67}{space 1}    0.43{col 76}{space 3}0.672{col 84}{space 4} -.065272{col 97}{space 3} .1002379
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0092439{col 56}{space 2} .0405381{col 67}{space 1}   -0.23{col 76}{space 3}0.821{col 84}{space 4}-.0909431{col 97}{space 3} .0724554
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0032968{col 56}{space 2} .0339056{col 67}{space 1}    0.10{col 76}{space 3}0.923{col 84}{space 4}-.0650354{col 97}{space 3} .0716291
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0071173{col 56}{space 2} .0340765{col 67}{space 1}   -0.21{col 76}{space 3}0.836{col 84}{space 4} -.075794{col 97}{space 3} .0615594
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} -.037952{col 56}{space 2} .0117034{col 67}{space 1}   -3.24{col 76}{space 3}0.002{col 84}{space 4}-.0615386{col 97}{space 3}-.0143653
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0008472{col 56}{space 2} .0005858{col 67}{space 1}   -1.45{col 76}{space 3}0.155{col 84}{space 4}-.0020278{col 97}{space 3} .0003335
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0837961{col 56}{space 2}  .022319{col 67}{space 1}   -3.75{col 76}{space 3}0.001{col 84}{space 4} -.128777{col 97}{space 3}-.0388151
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1664231{col 56}{space 2}  .014953{col 67}{space 1}  -11.13{col 76}{space 3}0.000{col 84}{space 4}-.1965589{col 97}{space 3}-.1362873
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0059094{col 56}{space 2} .0236086{col 67}{space 1}    0.25{col 76}{space 3}0.804{col 84}{space 4}-.0416706{col 97}{space 3} .0534895
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0534323{col 56}{space 2} .0248497{col 67}{space 1}    2.15{col 76}{space 3}0.037{col 84}{space 4} .0033509{col 97}{space 3} .1035136
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.1016211{col 56}{space 2} .0191091{col 67}{space 1}   -5.32{col 76}{space 3}0.000{col 84}{space 4}-.1401329{col 97}{space 3}-.0631093
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0277977{col 56}{space 2}  .019105{col 67}{space 1}    1.46{col 76}{space 3}0.153{col 84}{space 4}-.0107058{col 97}{space 3} .0663013
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1993  {c |}{col 44}{res}{space 2} .0198557{col 56}{space 2} .0130156{col 67}{space 1}    1.53{col 76}{space 3}0.134{col 84}{space 4}-.0063757{col 97}{space 3}  .046087
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .4380218{col 56}{space 2} .0712292{col 67}{space 1}    6.15{col 76}{space 3}0.000{col 84}{space 4} .2944688{col 97}{space 3} .5815748
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1992}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      4,761    .2778828    .4480023          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(44) = {res}    0.2111
{col 25}{txt}Prob>|t| = {res}    0.8048

95%{txt} confidence set for null hypothesis expression: {res}[−.1335, .3652]
{err}1992

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1989 {c |}{res}      2,401       50.43       50.43
{txt}       1993 {c |}{res}      2,360       49.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,761      100.00

{txt}Linear regression                               Number of obs     = {res}     7,384
                                                {txt}F(18, 48)         =  {res}    22.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0454
                                                {txt}Root MSE          =    {res} .40926

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0003565{col 56}{space 2} .0275398{col 67}{space 1}    0.01{col 76}{space 3}0.990{col 84}{space 4}-.0550159{col 97}{space 3}  .055729
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0770882{col 56}{space 2} .1521547{col 67}{space 1}   -0.51{col 76}{space 3}0.615{col 84}{space 4}-.3830157{col 97}{space 3} .2288392
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}  .033749{col 56}{space 2} .0278199{col 67}{space 1}    1.21{col 76}{space 3}0.231{col 84}{space 4}-.0221867{col 97}{space 3} .0896846
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}  .039701{col 56}{space 2} .0288102{col 67}{space 1}    1.38{col 76}{space 3}0.175{col 84}{space 4}-.0182258{col 97}{space 3} .0976278
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0333381{col 56}{space 2} .0298785{col 67}{space 1}    1.12{col 76}{space 3}0.270{col 84}{space 4}-.0267366{col 97}{space 3} .0934128
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0063884{col 56}{space 2} .0282002{col 67}{space 1}    0.23{col 76}{space 3}0.822{col 84}{space 4}-.0503119{col 97}{space 3} .0630887
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0009221{col 56}{space 2} .0301545{col 67}{space 1}    0.03{col 76}{space 3}0.976{col 84}{space 4}-.0597075{col 97}{space 3} .0615518
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0333244{col 56}{space 2} .0270443{col 67}{space 1}   -1.23{col 76}{space 3}0.224{col 84}{space 4}-.0877006{col 97}{space 3} .0210518
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0150836{col 56}{space 2} .0113248{col 67}{space 1}   -1.33{col 76}{space 3}0.189{col 84}{space 4}-.0378536{col 97}{space 3} .0076864
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0007563{col 56}{space 2} .0005493{col 67}{space 1}    1.38{col 76}{space 3}0.175{col 84}{space 4}-.0003481{col 97}{space 3} .0018608
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.1080129{col 56}{space 2} .0175568{col 67}{space 1}   -6.15{col 76}{space 3}0.000{col 84}{space 4}-.1433132{col 97}{space 3}-.0727127
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1067824{col 56}{space 2} .0166622{col 67}{space 1}   -6.41{col 76}{space 3}0.000{col 84}{space 4}-.1402841{col 97}{space 3}-.0732808
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0048918{col 56}{space 2} .0147542{col 67}{space 1}    0.33{col 76}{space 3}0.742{col 84}{space 4}-.0247734{col 97}{space 3}  .034557
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0726416{col 56}{space 2} .0213535{col 67}{space 1}    3.40{col 76}{space 3}0.001{col 84}{space 4} .0297076{col 97}{space 3} .1155756
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0250765{col 56}{space 2}  .016915{col 67}{space 1}   -1.48{col 76}{space 3}0.145{col 84}{space 4}-.0590864{col 97}{space 3} .0089335
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0168271{col 56}{space 2} .0191465{col 67}{space 1}    0.88{col 76}{space 3}0.384{col 84}{space 4}-.0216695{col 97}{space 3} .0553237
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2046  {c |}{col 44}{res}{space 2}-.0108462{col 56}{space 2} .0114818{col 67}{space 1}   -0.94{col 76}{space 3}0.350{col 84}{space 4} -.033932{col 97}{space 3} .0122395
{txt}{space 37}2050  {c |}{col 44}{res}{space 2} .0592491{col 56}{space 2} .0152851{col 67}{space 1}    3.88{col 76}{space 3}0.000{col 84}{space 4} .0285164{col 97}{space 3} .0899818
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2069609{col 56}{space 2} .0515218{col 67}{space 1}    4.02{col 76}{space 3}0.000{col 84}{space 4} .1033694{col 97}{space 3} .3105524
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1993}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,384    .2261647    .4183753          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(48) = {res}    0.0129
{col 25}{txt}Prob>|t| = {res}    0.9950

95%{txt} confidence set for null hypothesis expression: {res}[−.2907, .6905]
{err}1993

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2045 {c |}{res}      2,449       33.17       33.17
{txt}       2046 {c |}{res}      2,455       33.25       66.41
{txt}       2050 {c |}{res}      2,480       33.59      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,384      100.00

{txt}Linear regression                               Number of obs     = {res}     7,109
                                                {txt}F(18, 48)         =  {res}    17.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0370
                                                {txt}Root MSE          =    {res} .38769

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0020376{col 56}{space 2} .0116612{col 67}{space 1}    0.17{col 76}{space 3}0.862{col 84}{space 4}-.0214088{col 97}{space 3}  .025484
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.1200862{col 56}{space 2} .1328325{col 67}{space 1}   -0.90{col 76}{space 3}0.370{col 84}{space 4}-.3871637{col 97}{space 3} .1469914
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0189678{col 56}{space 2} .0235879{col 67}{space 1}    0.80{col 76}{space 3}0.425{col 84}{space 4}-.0284588{col 97}{space 3} .0663945
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0348636{col 56}{space 2} .0227325{col 67}{space 1}    1.53{col 76}{space 3}0.132{col 84}{space 4}-.0108432{col 97}{space 3} .0805704
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0404254{col 56}{space 2}  .024349{col 67}{space 1}   -1.66{col 76}{space 3}0.103{col 84}{space 4}-.0893823{col 97}{space 3} .0085315
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0157238{col 56}{space 2} .0247666{col 67}{space 1}   -0.63{col 76}{space 3}0.529{col 84}{space 4}-.0655203{col 97}{space 3} .0340728
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0078437{col 56}{space 2} .0337489{col 67}{space 1}   -0.23{col 76}{space 3}0.817{col 84}{space 4}-.0757004{col 97}{space 3} .0600131
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0210004{col 56}{space 2} .0273836{col 67}{space 1}   -0.77{col 76}{space 3}0.447{col 84}{space 4}-.0760589{col 97}{space 3} .0340581
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0249299{col 56}{space 2} .0105319{col 67}{space 1}   -2.37{col 76}{space 3}0.022{col 84}{space 4}-.0461057{col 97}{space 3}-.0037541
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0012413{col 56}{space 2} .0003644{col 67}{space 1}    3.41{col 76}{space 3}0.001{col 84}{space 4} .0005087{col 97}{space 3} .0019739
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0521468{col 56}{space 2} .0153812{col 67}{space 1}   -3.39{col 76}{space 3}0.001{col 84}{space 4}-.0830727{col 97}{space 3}-.0212209
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0836071{col 56}{space 2} .0133718{col 67}{space 1}   -6.25{col 76}{space 3}0.000{col 84}{space 4}-.1104928{col 97}{space 3}-.0567214
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0105702{col 56}{space 2} .0131864{col 67}{space 1}    0.80{col 76}{space 3}0.427{col 84}{space 4}-.0159429{col 97}{space 3} .0370833
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0836929{col 56}{space 2} .0207433{col 67}{space 1}    4.03{col 76}{space 3}0.000{col 84}{space 4} .0419857{col 97}{space 3} .1254001
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0007174{col 56}{space 2} .0174185{col 67}{space 1}   -0.04{col 76}{space 3}0.967{col 84}{space 4}-.0357398{col 97}{space 3} .0343049
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0548138{col 56}{space 2} .0127325{col 67}{space 1}    4.31{col 76}{space 3}0.000{col 84}{space 4} .0292134{col 97}{space 3} .0804142
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2083  {c |}{col 44}{res}{space 2} -.037245{col 56}{space 2} .0142266{col 67}{space 1}   -2.62{col 76}{space 3}0.012{col 84}{space 4}-.0658494{col 97}{space 3}-.0086406
{txt}{space 37}2085  {c |}{col 44}{res}{space 2}-.0378156{col 56}{space 2} .0110379{col 67}{space 1}   -3.43{col 76}{space 3}0.001{col 84}{space 4}-.0600088{col 97}{space 3}-.0156224
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2002171{col 56}{space 2} .0456682{col 67}{space 1}    4.38{col 76}{space 3}0.000{col 84}{space 4}  .108395{col 97}{space 3} .2920392
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1994}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,109    .1928541    .3945672          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(48) = {res}    0.1747
{col 25}{txt}Prob>|t| = {res}    0.8919

95%{txt} confidence set for null hypothesis expression: {res}[−.3099, .1619]
{err}1994

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2077 {c |}{res}      2,462       34.63       34.63
{txt}       2083 {c |}{res}      2,180       30.67       65.30
{txt}       2085 {c |}{res}      2,467       34.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,109      100.00

{txt}Linear regression                               Number of obs     = {res}     7,380
                                                {txt}F(18, 48)         =  {res}    34.76
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0470
                                                {txt}Root MSE          =    {res} .40332

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0121808{col 56}{space 2} .0374776{col 67}{space 1}   -0.33{col 76}{space 3}0.747{col 84}{space 4}-.0875345{col 97}{space 3} .0631729
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.1510159{col 56}{space 2} .1138405{col 67}{space 1}   -1.33{col 76}{space 3}0.191{col 84}{space 4}-.3799076{col 97}{space 3} .0778757
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0341573{col 56}{space 2} .0286055{col 67}{space 1}    1.19{col 76}{space 3}0.238{col 84}{space 4}-.0233578{col 97}{space 3} .0916724
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0241239{col 56}{space 2} .0258061{col 67}{space 1}    0.93{col 76}{space 3}0.355{col 84}{space 4}-.0277628{col 97}{space 3} .0760106
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0213498{col 56}{space 2} .0365153{col 67}{space 1}    0.58{col 76}{space 3}0.562{col 84}{space 4}-.0520691{col 97}{space 3} .0947687
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0114887{col 56}{space 2} .0293105{col 67}{space 1}   -0.39{col 76}{space 3}0.697{col 84}{space 4}-.0704213{col 97}{space 3}  .047444
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0057414{col 56}{space 2} .0500808{col 67}{space 1}    0.11{col 76}{space 3}0.909{col 84}{space 4}-.0949527{col 97}{space 3} .1064355
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0066571{col 56}{space 2} .0277204{col 67}{space 1}    0.24{col 76}{space 3}0.811{col 84}{space 4}-.0490786{col 97}{space 3} .0623927
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0139791{col 56}{space 2} .0114196{col 67}{space 1}   -1.22{col 76}{space 3}0.227{col 84}{space 4}-.0369398{col 97}{space 3} .0089815
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0014459{col 56}{space 2} .0003232{col 67}{space 1}    4.47{col 76}{space 3}0.000{col 84}{space 4}  .000796{col 97}{space 3} .0020957
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.1034905{col 56}{space 2}  .012327{col 67}{space 1}   -8.40{col 76}{space 3}0.000{col 84}{space 4}-.1282755{col 97}{space 3}-.0787055
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1156347{col 56}{space 2}  .014882{col 67}{space 1}   -7.77{col 76}{space 3}0.000{col 84}{space 4} -.145557{col 97}{space 3}-.0857125
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0498864{col 56}{space 2} .0151724{col 67}{space 1}    3.29{col 76}{space 3}0.002{col 84}{space 4} .0193804{col 97}{space 3} .0803925
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0723038{col 56}{space 2} .0177105{col 67}{space 1}    4.08{col 76}{space 3}0.000{col 84}{space 4} .0366944{col 97}{space 3} .1079133
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0119119{col 56}{space 2} .0145356{col 67}{space 1}   -0.82{col 76}{space 3}0.417{col 84}{space 4}-.0411378{col 97}{space 3} .0173139
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0348563{col 56}{space 2} .0146992{col 67}{space 1}    2.37{col 76}{space 3}0.022{col 84}{space 4} .0053016{col 97}{space 3}  .064411
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2132  {c |}{col 44}{res}{space 2}-.0245035{col 56}{space 2} .0113239{col 67}{space 1}   -2.16{col 76}{space 3}0.035{col 84}{space 4}-.0472718{col 97}{space 3}-.0017352
{txt}{space 37}2151  {c |}{col 44}{res}{space 2}-.0253409{col 56}{space 2}  .011726{col 67}{space 1}   -2.16{col 76}{space 3}0.036{col 84}{space 4}-.0489176{col 97}{space 3}-.0017642
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2172736{col 56}{space 2} .0475397{col 67}{space 1}    4.57{col 76}{space 3}0.000{col 84}{space 4} .1216887{col 97}{space 3} .3128586
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1995}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,380    .2176152    .4126523          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(48) = {res}   -0.3250
{col 25}{txt}Prob>|t| = {res}    0.7598

95%{txt} confidence set for null hypothesis expression: {res}[−.3421, .09209]
{err}1995

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2130 {c |}{res}      2,464       33.39       33.39
{txt}       2132 {c |}{res}      2,455       33.27       66.65
{txt}       2151 {c |}{res}      2,461       33.35      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,380      100.00

{txt}Linear regression                               Number of obs     = {res}     4,924
                                                {txt}F(17, 48)         =  {res}    11.78
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0284
                                                {txt}Root MSE          =    {res} .43011

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0201572{col 56}{space 2} .0191871{col 67}{space 1}   -1.05{col 76}{space 3}0.299{col 84}{space 4}-.0587355{col 97}{space 3}  .018421
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0750222{col 56}{space 2} .1704579{col 67}{space 1}   -0.44{col 76}{space 3}0.662{col 84}{space 4}-.4177509{col 97}{space 3} .2677065
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0287289{col 56}{space 2} .0267954{col 67}{space 1}    1.07{col 76}{space 3}0.289{col 84}{space 4}-.0251468{col 97}{space 3} .0826047
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}  .018847{col 56}{space 2} .0294902{col 67}{space 1}    0.64{col 76}{space 3}0.526{col 84}{space 4} -.040447{col 97}{space 3}  .078141
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0427165{col 56}{space 2}  .032847{col 67}{space 1}    1.30{col 76}{space 3}0.200{col 84}{space 4}-.0233269{col 97}{space 3} .1087598
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0152382{col 56}{space 2} .0271053{col 67}{space 1}    0.56{col 76}{space 3}0.577{col 84}{space 4}-.0392607{col 97}{space 3} .0697371
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0161164{col 56}{space 2} .0414147{col 67}{space 1}   -0.39{col 76}{space 3}0.699{col 84}{space 4}-.0993862{col 97}{space 3} .0671534
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0101511{col 56}{space 2} .0345394{col 67}{space 1}    0.29{col 76}{space 3}0.770{col 84}{space 4}-.0592951{col 97}{space 3} .0795973
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0207469{col 56}{space 2} .0150817{col 67}{space 1}    1.38{col 76}{space 3}0.175{col 84}{space 4}-.0095769{col 97}{space 3} .0510708
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0001029{col 56}{space 2} .0004403{col 67}{space 1}   -0.23{col 76}{space 3}0.816{col 84}{space 4}-.0009881{col 97}{space 3} .0007823
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.086092{col 56}{space 2} .0220228{col 67}{space 1}   -3.91{col 76}{space 3}0.000{col 84}{space 4}-.1303717{col 97}{space 3}-.0418123
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1178723{col 56}{space 2} .0173914{col 67}{space 1}   -6.78{col 76}{space 3}0.000{col 84}{space 4}-.1528401{col 97}{space 3}-.0829045
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0051014{col 56}{space 2} .0229656{col 67}{space 1}    0.22{col 76}{space 3}0.825{col 84}{space 4} -.041074{col 97}{space 3} .0512768
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0760481{col 56}{space 2} .0241105{col 67}{space 1}    3.15{col 76}{space 3}0.003{col 84}{space 4} .0275706{col 97}{space 3} .1245256
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0580797{col 56}{space 2} .0246142{col 67}{space 1}   -2.36{col 76}{space 3}0.022{col 84}{space 4}-.1075699{col 97}{space 3}-.0085895
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0091516{col 56}{space 2} .0207459{col 67}{space 1}    0.44{col 76}{space 3}0.661{col 84}{space 4}-.0325608{col 97}{space 3}  .050864
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2208  {c |}{col 44}{res}{space 2}  .024192{col 56}{space 2} .0141141{col 67}{space 1}    1.71{col 76}{space 3}0.093{col 84}{space 4}-.0041863{col 97}{space 3} .0525702
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2618219{col 56}{space 2}  .053283{col 67}{space 1}    4.91{col 76}{space 3}0.000{col 84}{space 4} .1546893{col 97}{space 3} .3689545
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1996}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      4,924    .2544679    .4356059          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(48) = {res}   -1.0506
{col 25}{txt}Prob>|t| = {res}    0.8799

95%{txt} confidence set for null hypothesis expression: {res}[−.637, .4408]
{err}1996

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2204 {c |}{res}      2,460       49.96       49.96
{txt}       2208 {c |}{res}      2,464       50.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,924      100.00
{txt}{p 0 6 2}note: {bf:2233.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,444
                                                {txt}F(16, 45)         =  {res}     4.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0226
                                                {txt}Root MSE          =    {res} .41537

{txt}{ralign 108:(Std. err. adjusted for {res:46} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}  .171277{col 56}{space 2} .0637148{col 67}{space 1}    2.69{col 76}{space 3}0.010{col 84}{space 4} .0429487{col 97}{space 3} .2996053
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0225363{col 56}{space 2} .2054138{col 67}{space 1}   -0.11{col 76}{space 3}0.913{col 84}{space 4}-.4362608{col 97}{space 3} .3911883
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0393205{col 56}{space 2} .0438982{col 67}{space 1}   -0.90{col 76}{space 3}0.375{col 84}{space 4}-.1277359{col 97}{space 3} .0490949
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0719382{col 56}{space 2} .0438802{col 67}{space 1}   -1.64{col 76}{space 3}0.108{col 84}{space 4}-.1603175{col 97}{space 3} .0164412
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0461708{col 56}{space 2} .0523135{col 67}{space 1}   -0.88{col 76}{space 3}0.382{col 84}{space 4}-.1515355{col 97}{space 3} .0591939
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0748516{col 56}{space 2} .0428892{col 67}{space 1}   -1.75{col 76}{space 3}0.088{col 84}{space 4}-.1612348{col 97}{space 3} .0115316
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0624332{col 56}{space 2} .0554567{col 67}{space 1}   -1.13{col 76}{space 3}0.266{col 84}{space 4}-.1741288{col 97}{space 3} .0492624
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} -.079912{col 56}{space 2} .0554069{col 67}{space 1}   -1.44{col 76}{space 3}0.156{col 84}{space 4}-.1915072{col 97}{space 3} .0316833
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0274548{col 56}{space 2}  .019026{col 67}{space 1}   -1.44{col 76}{space 3}0.156{col 84}{space 4}-.0657751{col 97}{space 3} .0108656
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0023063{col 56}{space 2} .0008504{col 67}{space 1}    2.71{col 76}{space 3}0.009{col 84}{space 4} .0005936{col 97}{space 3} .0040191
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0284057{col 56}{space 2} .0229169{col 67}{space 1}    1.24{col 76}{space 3}0.222{col 84}{space 4}-.0177513{col 97}{space 3} .0745626
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .1004254{col 56}{space 2} .0232683{col 67}{space 1}    4.32{col 76}{space 3}0.000{col 84}{space 4} .0535607{col 97}{space 3} .1472902
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0081049{col 56}{space 2} .0179041{col 67}{space 1}    0.45{col 76}{space 3}0.653{col 84}{space 4}-.0279558{col 97}{space 3} .0441656
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0196647{col 56}{space 2} .0332965{col 67}{space 1}   -0.59{col 76}{space 3}0.558{col 84}{space 4}-.0867273{col 97}{space 3} .0473978
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0440142{col 56}{space 2} .0273093{col 67}{space 1}    1.61{col 76}{space 3}0.114{col 84}{space 4}-.0109895{col 97}{space 3} .0990178
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}  .020957{col 56}{space 2} .0233643{col 67}{space 1}    0.90{col 76}{space 3}0.375{col 84}{space 4}-.0261011{col 97}{space 3} .0680152
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2233  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1621181{col 56}{space 2} .0742463{col 67}{space 1}    2.18{col 76}{space 3}0.034{col 84}{space 4} .0125784{col 97}{space 3} .3116577
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1997}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,444    .2266776     .418768          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(45) = {res}    2.6882
{col 25}{txt}Prob>|t| = {res}    0.0811

95%{txt} confidence set for null hypothesis expression: {res}[−.05839, .5845]
{err}1997

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2233 {c |}{res}      2,444      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,444      100.00
{txt}{p 0 6 2}note: {bf:2274.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,468
                                                {txt}F(16, 48)         =  {res}     5.20
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0205
                                                {txt}Root MSE          =    {res} .42601

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0209428{col 56}{space 2} .0095171{col 67}{space 1}   -2.20{col 76}{space 3}0.033{col 84}{space 4}-.0400782{col 97}{space 3}-.0018074
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .4732978{col 56}{space 2} .2085554{col 67}{space 1}    2.27{col 76}{space 3}0.028{col 84}{space 4}  .053969{col 97}{space 3} .8926266
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} -.052657{col 56}{space 2} .0334113{col 67}{space 1}   -1.58{col 76}{space 3}0.122{col 84}{space 4} -.119835{col 97}{space 3} .0145209
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0573219{col 56}{space 2} .0373801{col 67}{space 1}   -1.53{col 76}{space 3}0.132{col 84}{space 4}-.1324795{col 97}{space 3} .0178358
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.1018302{col 56}{space 2} .0376729{col 67}{space 1}   -2.70{col 76}{space 3}0.009{col 84}{space 4}-.1775767{col 97}{space 3}-.0260836
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0368474{col 56}{space 2} .0462011{col 67}{space 1}   -0.80{col 76}{space 3}0.429{col 84}{space 4}-.1297409{col 97}{space 3} .0560461
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.1151264{col 56}{space 2} .0409879{col 67}{space 1}   -2.81{col 76}{space 3}0.007{col 84}{space 4}-.1975381{col 97}{space 3}-.0327148
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0883291{col 56}{space 2} .0366476{col 67}{space 1}   -2.41{col 76}{space 3}0.020{col 84}{space 4}-.1620141{col 97}{space 3}-.0146441
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0197252{col 56}{space 2} .0167739{col 67}{space 1}   -1.18{col 76}{space 3}0.245{col 84}{space 4}-.0534515{col 97}{space 3} .0140011
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0014279{col 56}{space 2} .0008294{col 67}{space 1}    1.72{col 76}{space 3}0.092{col 84}{space 4}-.0002397{col 97}{space 3} .0030956
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0339916{col 56}{space 2} .0265831{col 67}{space 1}    1.28{col 76}{space 3}0.207{col 84}{space 4}-.0194573{col 97}{space 3} .0874404
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}  .046326{col 56}{space 2} .0407746{col 67}{space 1}    1.14{col 76}{space 3}0.262{col 84}{space 4}-.0356568{col 97}{space 3} .1283087
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0773982{col 56}{space 2} .0314873{col 67}{space 1}   -2.46{col 76}{space 3}0.018{col 84}{space 4}-.1407077{col 97}{space 3}-.0140888
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0069506{col 56}{space 2} .0325822{col 67}{space 1}   -0.21{col 76}{space 3}0.832{col 84}{space 4}-.0724615{col 97}{space 3} .0585604
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0133035{col 56}{space 2} .0355065{col 67}{space 1}   -0.37{col 76}{space 3}0.710{col 84}{space 4}-.0846941{col 97}{space 3} .0580871
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0425128{col 56}{space 2} .0339881{col 67}{space 1}    1.25{col 76}{space 3}0.217{col 84}{space 4}-.0258248{col 97}{space 3} .1108504
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2274  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1090785{col 56}{space 2} .0746154{col 67}{space 1}    1.46{col 76}{space 3}0.150{col 84}{space 4}-.0409458{col 97}{space 3} .2591028
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1998}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,468    .2431118     .429049          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(48) = {res}   -2.2005
{col 25}{txt}Prob>|t| = {res}    0.2242

95%{txt} confidence set for null hypothesis expression: {res}[−.4687, .4449]
{err}1998

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2274 {c |}{res}      2,468      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,468      100.00
{txt}{p 0 6 2}note: {bf:2316.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,476
                                                {txt}F(16, 46)         =  {res}     4.30
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0239
                                                {txt}Root MSE          =    {res} .43877

{txt}{ralign 108:(Std. err. adjusted for {res:47} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.1067048{col 56}{space 2} .0629771{col 67}{space 1}   -1.69{col 76}{space 3}0.097{col 84}{space 4}-.2334711{col 97}{space 3} .0200614
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .1431915{col 56}{space 2} .2468835{col 67}{space 1}    0.58{col 76}{space 3}0.565{col 84}{space 4}-.3537592{col 97}{space 3} .6401422
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0626855{col 56}{space 2} .0494476{col 67}{space 1}   -1.27{col 76}{space 3}0.211{col 84}{space 4}-.1622183{col 97}{space 3} .0368472
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0587539{col 56}{space 2} .0478516{col 67}{space 1}   -1.23{col 76}{space 3}0.226{col 84}{space 4}-.1550741{col 97}{space 3} .0375663
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.1624745{col 56}{space 2}  .054608{col 67}{space 1}   -2.98{col 76}{space 3}0.005{col 84}{space 4}-.2723948{col 97}{space 3}-.0525543
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0786643{col 56}{space 2} .0455566{col 67}{space 1}   -1.73{col 76}{space 3}0.091{col 84}{space 4} -.170365{col 97}{space 3} .0130363
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0738637{col 56}{space 2} .0564503{col 67}{space 1}   -1.31{col 76}{space 3}0.197{col 84}{space 4}-.1874922{col 97}{space 3} .0397648
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0942108{col 56}{space 2} .0682879{col 67}{space 1}   -1.38{col 76}{space 3}0.174{col 84}{space 4}-.2316672{col 97}{space 3} .0432456
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0331652{col 56}{space 2}  .021757{col 67}{space 1}   -1.52{col 76}{space 3}0.134{col 84}{space 4}-.0769598{col 97}{space 3} .0106293
{txt}{space 39}age {c |}{col 44}{res}{space 2}   .00155{col 56}{space 2} .0008001{col 67}{space 1}    1.94{col 76}{space 3}0.059{col 84}{space 4}-.0000605{col 97}{space 3} .0031606
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0330758{col 56}{space 2} .0280422{col 67}{space 1}    1.18{col 76}{space 3}0.244{col 84}{space 4}-.0233702{col 97}{space 3} .0895218
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0968307{col 56}{space 2} .0540063{col 67}{space 1}    1.79{col 76}{space 3}0.080{col 84}{space 4}-.0118783{col 97}{space 3} .2055397
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0513087{col 56}{space 2} .0295338{col 67}{space 1}   -1.74{col 76}{space 3}0.089{col 84}{space 4}-.1107571{col 97}{space 3} .0081398
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0100905{col 56}{space 2}   .03036{col 67}{space 1}    0.33{col 76}{space 3}0.741{col 84}{space 4} -.051021{col 97}{space 3} .0712021
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0867959{col 56}{space 2} .0460598{col 67}{space 1}    1.88{col 76}{space 3}0.066{col 84}{space 4}-.0059176{col 97}{space 3} .1795094
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0702382{col 56}{space 2}  .026991{col 67}{space 1}    2.60{col 76}{space 3}0.012{col 84}{space 4} .0159082{col 97}{space 3} .1245683
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2316  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  .218243{col 56}{space 2}  .100151{col 67}{space 1}    2.18{col 76}{space 3}0.034{col 84}{space 4} .0166495{col 97}{space 3} .4198365
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1999}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,476    .2673667    .4426747          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(46) = {res}   -1.6943
{col 25}{txt}Prob>|t| = {res}    0.3894

95%{txt} confidence set for null hypothesis expression: {res}[−.455, .1673]
{err}1999

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2316 {c |}{res}      2,476      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,476      100.00
{txt}{p 0 6 2}note: {bf:2382.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    23,428
                                                {txt}F(16, 49)         =  {res}    23.95
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0204
                                                {txt}Root MSE          =    {res} .45592

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .1126349{col 56}{space 2} .0191539{col 67}{space 1}    5.88{col 76}{space 3}0.000{col 84}{space 4} .0741437{col 97}{space 3} .1511262
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .3326405{col 56}{space 2} .1793997{col 67}{space 1}    1.85{col 76}{space 3}0.070{col 84}{space 4}-.0278766{col 97}{space 3} .6931576
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0489653{col 56}{space 2} .0210348{col 67}{space 1}   -2.33{col 76}{space 3}0.024{col 84}{space 4}-.0912363{col 97}{space 3}-.0066942
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0727548{col 56}{space 2} .0237888{col 67}{space 1}   -3.06{col 76}{space 3}0.004{col 84}{space 4}-.1205603{col 97}{space 3}-.0249494
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0764463{col 56}{space 2} .0226566{col 67}{space 1}   -3.37{col 76}{space 3}0.001{col 84}{space 4}-.1219764{col 97}{space 3}-.0309161
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0582595{col 56}{space 2} .0233989{col 67}{space 1}   -2.49{col 76}{space 3}0.016{col 84}{space 4}-.1052813{col 97}{space 3}-.0112378
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0219558{col 56}{space 2} .0229874{col 67}{space 1}   -0.96{col 76}{space 3}0.344{col 84}{space 4}-.0681508{col 97}{space 3} .0242391
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.1177759{col 56}{space 2} .0549014{col 67}{space 1}   -2.15{col 76}{space 3}0.037{col 84}{space 4}-.2281044{col 97}{space 3}-.0074474
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0115019{col 56}{space 2} .0070361{col 67}{space 1}   -1.63{col 76}{space 3}0.109{col 84}{space 4}-.0256414{col 97}{space 3} .0026376
{txt}{space 39}age {c |}{col 44}{res}{space 2}   .00166{col 56}{space 2}  .000327{col 67}{space 1}    5.08{col 76}{space 3}0.000{col 84}{space 4} .0010029{col 97}{space 3} .0023172
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0236118{col 56}{space 2} .0130992{col 67}{space 1}    1.80{col 76}{space 3}0.078{col 84}{space 4} -.002712{col 97}{space 3} .0499355
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0439473{col 56}{space 2} .0177389{col 67}{space 1}    2.48{col 76}{space 3}0.017{col 84}{space 4} .0082996{col 97}{space 3}  .079595
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0401919{col 56}{space 2}  .013368{col 67}{space 1}   -3.01{col 76}{space 3}0.004{col 84}{space 4}-.0670559{col 97}{space 3}-.0133278
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0130047{col 56}{space 2} .0120396{col 67}{space 1}    1.08{col 76}{space 3}0.285{col 84}{space 4}-.0111897{col 97}{space 3} .0371992
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0079336{col 56}{space 2}  .014457{col 67}{space 1}   -0.55{col 76}{space 3}0.586{col 84}{space 4} -.036986{col 97}{space 3} .0211189
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0149398{col 56}{space 2} .0142163{col 67}{space 1}    1.05{col 76}{space 3}0.298{col 84}{space 4} -.013629{col 97}{space 3} .0435085
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2382  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1725854{col 56}{space 2} .0600393{col 67}{space 1}    2.87{col 76}{space 3}0.006{col 84}{space 4} .0519319{col 97}{space 3} .2932388
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2000}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     23,428    .3051477    .4604798          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    5.8805
{col 25}{txt}Prob>|t| = {res}    0.1211

95%{txt} confidence set for null hypothesis expression: {res}[−.1437, .8639]
{err}2000

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2382 {c |}{res}     23,428      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     23,428      100.00
{txt}{p 0 6 2}note: {bf:2406.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,479
                                                {txt}F(16, 46)         =  {res}     2.48
                                                {txt}Prob > F          = {res}    0.0084
                                                {txt}R-squared         = {res}    0.0117
                                                {txt}Root MSE          =    {res} .44445

{txt}{ralign 108:(Std. err. adjusted for {res:47} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0044194{col 56}{space 2} .0031502{col 67}{space 1}   -1.40{col 76}{space 3}0.167{col 84}{space 4}-.0107603{col 97}{space 3} .0019216
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .2298486{col 56}{space 2} .2203213{col 67}{space 1}    1.04{col 76}{space 3}0.302{col 84}{space 4}-.2136352{col 97}{space 3} .6733324
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0101459{col 56}{space 2} .0427966{col 67}{space 1}    0.24{col 76}{space 3}0.814{col 84}{space 4}-.0759991{col 97}{space 3} .0962909
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0294944{col 56}{space 2} .0398602{col 67}{space 1}   -0.74{col 76}{space 3}0.463{col 84}{space 4}-.1097289{col 97}{space 3} .0507401
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0524397{col 56}{space 2} .0475161{col 67}{space 1}   -1.10{col 76}{space 3}0.275{col 84}{space 4}-.1480846{col 97}{space 3} .0432052
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0112808{col 56}{space 2} .0445736{col 67}{space 1}   -0.25{col 76}{space 3}0.801{col 84}{space 4}-.1010027{col 97}{space 3} .0784412
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0082679{col 56}{space 2}  .047117{col 67}{space 1}   -0.18{col 76}{space 3}0.861{col 84}{space 4}-.1031094{col 97}{space 3} .0865736
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0713753{col 56}{space 2} .0660746{col 67}{space 1}   -1.08{col 76}{space 3}0.286{col 84}{space 4}-.2043766{col 97}{space 3} .0616261
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0031046{col 56}{space 2}  .023839{col 67}{space 1}    0.13{col 76}{space 3}0.897{col 84}{space 4}-.0448808{col 97}{space 3}   .05109
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0007342{col 56}{space 2} .0008295{col 67}{space 1}    0.89{col 76}{space 3}0.381{col 84}{space 4}-.0009354{col 97}{space 3} .0024039
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0006026{col 56}{space 2} .0269616{col 67}{space 1}    0.02{col 76}{space 3}0.982{col 84}{space 4}-.0536683{col 97}{space 3} .0548734
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0410153{col 56}{space 2} .0337338{col 67}{space 1}    1.22{col 76}{space 3}0.230{col 84}{space 4}-.0268872{col 97}{space 3} .1089178
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0092809{col 56}{space 2} .0302123{col 67}{space 1}   -0.31{col 76}{space 3}0.760{col 84}{space 4}-.0700951{col 97}{space 3} .0515332
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0419762{col 56}{space 2} .0326507{col 67}{space 1}    1.29{col 76}{space 3}0.205{col 84}{space 4}-.0237462{col 97}{space 3} .1076985
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0407255{col 56}{space 2} .0341408{col 67}{space 1}    1.19{col 76}{space 3}0.239{col 84}{space 4}-.0279963{col 97}{space 3} .1094473
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0651469{col 56}{space 2} .0317596{col 67}{space 1}    2.05{col 76}{space 3}0.046{col 84}{space 4}  .001218{col 97}{space 3} .1290757
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2406  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1676623{col 56}{space 2} .0861694{col 67}{space 1}    1.95{col 76}{space 3}0.058{col 84}{space 4}-.0057877{col 97}{space 3} .3411123
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2001}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,479     .273094    .4456386          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(46) = {res}   -1.4029
{col 25}{txt}Prob>|t| = {res}    0.5065

95%{txt} confidence set for null hypothesis expression: {res}[−.1068, .1469]
{err}2001

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2406 {c |}{res}      2,479      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,479      100.00
{txt}{p 0 6 2}note: {bf:2444.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,488
                                                {txt}F(16, 47)         =  {res}     8.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0335
                                                {txt}Root MSE          =    {res} .44864

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}  .096693{col 56}{space 2} .0269521{col 67}{space 1}    3.59{col 76}{space 3}0.001{col 84}{space 4} .0424725{col 97}{space 3} .1509136
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .2678072{col 56}{space 2} .1981715{col 67}{space 1}    1.35{col 76}{space 3}0.183{col 84}{space 4}-.1308624{col 97}{space 3} .6664768
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0674118{col 56}{space 2} .0475428{col 67}{space 1}   -1.42{col 76}{space 3}0.163{col 84}{space 4}-.1630557{col 97}{space 3}  .028232
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.1020776{col 56}{space 2} .0483547{col 67}{space 1}   -2.11{col 76}{space 3}0.040{col 84}{space 4}-.1993548{col 97}{space 3}-.0048004
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.1801092{col 56}{space 2} .0490347{col 67}{space 1}   -3.67{col 76}{space 3}0.001{col 84}{space 4}-.2787542{col 97}{space 3}-.0814642
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} -.075179{col 56}{space 2} .0509051{col 67}{space 1}   -1.48{col 76}{space 3}0.146{col 84}{space 4}-.1775868{col 97}{space 3} .0272287
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0982896{col 56}{space 2} .0407173{col 67}{space 1}   -2.41{col 76}{space 3}0.020{col 84}{space 4}-.1802022{col 97}{space 3}-.0163769
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.1032146{col 56}{space 2} .0533151{col 67}{space 1}   -1.94{col 76}{space 3}0.059{col 84}{space 4}-.2104707{col 97}{space 3} .0040415
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}  -.03946{col 56}{space 2} .0152528{col 67}{space 1}   -2.59{col 76}{space 3}0.013{col 84}{space 4}-.0701446{col 97}{space 3}-.0087753
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0020216{col 56}{space 2} .0008618{col 67}{space 1}    2.35{col 76}{space 3}0.023{col 84}{space 4} .0002879{col 97}{space 3} .0037554
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0741259{col 56}{space 2}  .032587{col 67}{space 1}    2.27{col 76}{space 3}0.028{col 84}{space 4} .0085695{col 97}{space 3} .1396824
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0272904{col 56}{space 2} .0282021{col 67}{space 1}    0.97{col 76}{space 3}0.338{col 84}{space 4} -.029445{col 97}{space 3} .0840258
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}  -.04208{col 56}{space 2} .0284997{col 67}{space 1}   -1.48{col 76}{space 3}0.146{col 84}{space 4}-.0994141{col 97}{space 3} .0152541
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0530375{col 56}{space 2} .0253147{col 67}{space 1}    2.10{col 76}{space 3}0.042{col 84}{space 4} .0021108{col 97}{space 3} .1039642
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0598618{col 56}{space 2} .0327469{col 67}{space 1}   -1.83{col 76}{space 3}0.074{col 84}{space 4}  -.12574{col 97}{space 3} .0060164
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0393496{col 56}{space 2} .0280134{col 67}{space 1}    1.40{col 76}{space 3}0.167{col 84}{space 4}-.0170061{col 97}{space 3} .0957053
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2444  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1941359{col 56}{space 2} .0789279{col 67}{space 1}    2.46{col 76}{space 3}0.018{col 84}{space 4} .0353534{col 97}{space 3} .3529184
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2002}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,488    .2922026    .4548663          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    3.5876
{col 25}{txt}Prob>|t| = {res}    0.1672

95%{txt} confidence set for null hypothesis expression: {res}[−.09989, .3542]
{err}2002

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2444 {c |}{res}      2,488      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,488      100.00
{txt}{p 0 6 2}note: {bf:2477.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,461
                                                {txt}F(16, 47)         =  {res}     6.06
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0258
                                                {txt}Root MSE          =    {res} .41808

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0146906{col 56}{space 2} .0284901{col 67}{space 1}    0.52{col 76}{space 3}0.609{col 84}{space 4} -.042624{col 97}{space 3} .0720052
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0712712{col 56}{space 2} .1937253{col 67}{space 1}    0.37{col 76}{space 3}0.715{col 84}{space 4}-.3184538{col 97}{space 3} .4609961
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0482704{col 56}{space 2} .0443872{col 67}{space 1}   -1.09{col 76}{space 3}0.282{col 84}{space 4}-.1375659{col 97}{space 3} .0410251
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0407856{col 56}{space 2} .0342256{col 67}{space 1}   -1.19{col 76}{space 3}0.239{col 84}{space 4}-.1096386{col 97}{space 3} .0280673
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.1222542{col 56}{space 2} .0399814{col 67}{space 1}   -3.06{col 76}{space 3}0.004{col 84}{space 4}-.2026864{col 97}{space 3} -.041822
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0320131{col 56}{space 2} .0406873{col 67}{space 1}   -0.79{col 76}{space 3}0.435{col 84}{space 4}-.1138653{col 97}{space 3} .0498391
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0040608{col 56}{space 2} .0520529{col 67}{space 1}    0.08{col 76}{space 3}0.938{col 84}{space 4}-.1006561{col 97}{space 3} .1087777
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0648925{col 56}{space 2} .0638632{col 67}{space 1}   -1.02{col 76}{space 3}0.315{col 84}{space 4}-.1933687{col 97}{space 3} .0635837
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0230644{col 56}{space 2} .0215365{col 67}{space 1}   -1.07{col 76}{space 3}0.290{col 84}{space 4}-.0663902{col 97}{space 3} .0202614
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0024352{col 56}{space 2} .0009775{col 67}{space 1}    2.49{col 76}{space 3}0.016{col 84}{space 4} .0004687{col 97}{space 3} .0044017
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0050423{col 56}{space 2} .0264579{col 67}{space 1}    0.19{col 76}{space 3}0.850{col 84}{space 4}-.0481841{col 97}{space 3} .0582687
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0121493{col 56}{space 2} .0293168{col 67}{space 1}   -0.41{col 76}{space 3}0.680{col 84}{space 4}-.0711271{col 97}{space 3} .0468284
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0453481{col 56}{space 2} .0243273{col 67}{space 1}   -1.86{col 76}{space 3}0.069{col 84}{space 4}-.0942883{col 97}{space 3} .0035922
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}  .019054{col 56}{space 2} .0290507{col 67}{space 1}    0.66{col 76}{space 3}0.515{col 84}{space 4}-.0393884{col 97}{space 3} .0774964
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0075367{col 56}{space 2} .0330562{col 67}{space 1}   -0.23{col 76}{space 3}0.821{col 84}{space 4}-.0740372{col 97}{space 3} .0589637
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0252186{col 56}{space 2} .0333387{col 67}{space 1}    0.76{col 76}{space 3}0.453{col 84}{space 4}-.0418503{col 97}{space 3} .0922875
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2477  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1522176{col 56}{space 2} .0741903{col 67}{space 1}    2.05{col 76}{space 3}0.046{col 84}{space 4}  .002966{col 97}{space 3} .3014692
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2003}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,461    .2320195    .4222072          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    0.5156
{col 25}{txt}Prob>|t| = {res}    0.6386

95%{txt} confidence set for null hypothesis expression: {res}[−.07722, .1358]
{err}2003

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2477 {c |}{res}      2,461      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,461      100.00
{txt}{p 0 6 2}note: {bf:2555.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    23,526
                                                {txt}F(16, 49)         =  {res}    12.38
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0248
                                                {txt}Root MSE          =    {res} .43967

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} -.064158{col 56}{space 2} .0992157{col 67}{space 1}   -0.65{col 76}{space 3}0.521{col 84}{space 4}-.2635395{col 97}{space 3} .1352234
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .3801204{col 56}{space 2} .1353186{col 67}{space 1}    2.81{col 76}{space 3}0.007{col 84}{space 4} .1081875{col 97}{space 3} .6520533
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0278247{col 56}{space 2} .0223053{col 67}{space 1}   -1.25{col 76}{space 3}0.218{col 84}{space 4}-.0726488{col 97}{space 3} .0169994
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0581195{col 56}{space 2} .0251984{col 67}{space 1}   -2.31{col 76}{space 3}0.025{col 84}{space 4}-.1087576{col 97}{space 3}-.0074814
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} -.049911{col 56}{space 2} .0262122{col 67}{space 1}   -1.90{col 76}{space 3}0.063{col 84}{space 4}-.1025864{col 97}{space 3} .0027644
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0366281{col 56}{space 2} .0261324{col 67}{space 1}   -1.40{col 76}{space 3}0.167{col 84}{space 4} -.089143{col 97}{space 3} .0158869
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0285325{col 56}{space 2} .0368509{col 67}{space 1}   -0.77{col 76}{space 3}0.442{col 84}{space 4}-.1025871{col 97}{space 3} .0455222
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0715385{col 56}{space 2} .0636936{col 67}{space 1}   -1.12{col 76}{space 3}0.267{col 84}{space 4}-.1995356{col 97}{space 3} .0564587
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} -.048348{col 56}{space 2} .0063786{col 67}{space 1}   -7.58{col 76}{space 3}0.000{col 84}{space 4}-.0611664{col 97}{space 3}-.0355296
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0024112{col 56}{space 2} .0004072{col 67}{space 1}    5.92{col 76}{space 3}0.000{col 84}{space 4} .0015928{col 97}{space 3} .0032295
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0237495{col 56}{space 2} .0136396{col 67}{space 1}    1.74{col 76}{space 3}0.088{col 84}{space 4}-.0036603{col 97}{space 3} .0511593
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0391353{col 56}{space 2} .0187788{col 67}{space 1}    2.08{col 76}{space 3}0.042{col 84}{space 4}  .001398{col 97}{space 3} .0768727
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0381156{col 56}{space 2} .0120373{col 67}{space 1}   -3.17{col 76}{space 3}0.003{col 84}{space 4}-.0623055{col 97}{space 3}-.0139258
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0099088{col 56}{space 2} .0120782{col 67}{space 1}    0.82{col 76}{space 3}0.416{col 84}{space 4}-.0143631{col 97}{space 3} .0341808
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0046377{col 56}{space 2} .0140181{col 67}{space 1}   -0.33{col 76}{space 3}0.742{col 84}{space 4}-.0328081{col 97}{space 3} .0235327
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}  .027941{col 56}{space 2} .0119479{col 67}{space 1}    2.34{col 76}{space 3}0.023{col 84}{space 4} .0039308{col 97}{space 3} .0519511
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2555  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .0951988{col 56}{space 2} .0515416{col 67}{space 1}    1.85{col 76}{space 3}0.071{col 84}{space 4} -.008378{col 97}{space 3} .1987755
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2004}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     23,526    .2721245    .4450631          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.6467
{col 25}{txt}Prob>|t| = {res}    0.8338

95%{txt} confidence set for null hypothesis expression: {res}[−.2386, .2698]
{err}2004

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2555 {c |}{res}     23,526      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     23,526      100.00
{txt}{p 0 6 2}note: {bf:2589.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,471
                                                {txt}F(16, 44)         =  {res}     1.87
                                                {txt}Prob > F          = {res}    0.0516
                                                {txt}R-squared         = {res}    0.0121
                                                {txt}Root MSE          =    {res} .48061

{txt}{ralign 108:(Std. err. adjusted for {res:45} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0120622{col 56}{space 2} .0207659{col 67}{space 1}   -0.58{col 76}{space 3}0.564{col 84}{space 4}-.0539132{col 97}{space 3} .0297888
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.1632745{col 56}{space 2} .1867511{col 67}{space 1}   -0.87{col 76}{space 3}0.387{col 84}{space 4}-.5396465{col 97}{space 3} .2130976
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .1525591{col 56}{space 2} .0494689{col 67}{space 1}    3.08{col 76}{space 3}0.004{col 84}{space 4}  .052861{col 97}{space 3} .2522572
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}  .106655{col 56}{space 2} .0429109{col 67}{space 1}    2.49{col 76}{space 3}0.017{col 84}{space 4} .0201737{col 97}{space 3} .1931362
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .1026313{col 56}{space 2} .0483311{col 67}{space 1}    2.12{col 76}{space 3}0.039{col 84}{space 4} .0052263{col 97}{space 3} .2000363
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .1254786{col 56}{space 2} .0463892{col 67}{space 1}    2.70{col 76}{space 3}0.010{col 84}{space 4} .0319874{col 97}{space 3} .2189699
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .1335214{col 56}{space 2} .0716857{col 67}{space 1}    1.86{col 76}{space 3}0.069{col 84}{space 4}-.0109516{col 97}{space 3} .2779943
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .1076849{col 56}{space 2} .0521497{col 67}{space 1}    2.06{col 76}{space 3}0.045{col 84}{space 4} .0025841{col 97}{space 3} .2127857
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0187567{col 56}{space 2} .0198695{col 67}{space 1}    0.94{col 76}{space 3}0.350{col 84}{space 4}-.0212877{col 97}{space 3} .0588011
{txt}{space 39}age {c |}{col 44}{res}{space 2}  -.00146{col 56}{space 2} .0009044{col 67}{space 1}   -1.61{col 76}{space 3}0.114{col 84}{space 4}-.0032827{col 97}{space 3} .0003628
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0655923{col 56}{space 2} .0237501{col 67}{space 1}   -2.76{col 76}{space 3}0.008{col 84}{space 4}-.1134574{col 97}{space 3}-.0177272
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} -.076527{col 56}{space 2} .0308461{col 67}{space 1}   -2.48{col 76}{space 3}0.017{col 84}{space 4}-.1386933{col 97}{space 3}-.0143608
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0109282{col 56}{space 2} .0387472{col 67}{space 1}    0.28{col 76}{space 3}0.779{col 84}{space 4}-.0671617{col 97}{space 3}  .089018
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0093096{col 56}{space 2} .0352817{col 67}{space 1}    0.26{col 76}{space 3}0.793{col 84}{space 4}-.0617961{col 97}{space 3} .0804152
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0047997{col 56}{space 2}  .047936{col 67}{space 1}    0.10{col 76}{space 3}0.921{col 84}{space 4} -.091809{col 97}{space 3} .1014084
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0161127{col 56}{space 2} .0377579{col 67}{space 1}   -0.43{col 76}{space 3}0.672{col 84}{space 4}-.0922087{col 97}{space 3} .0599832
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2589  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3889924{col 56}{space 2} .0820423{col 67}{space 1}    4.74{col 76}{space 3}0.000{col 84}{space 4} .2236471{col 97}{space 3} .5543377
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2005}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,471    .3666532    .4819882          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(44) = {res}   -0.5809
{col 25}{txt}Prob>|t| = {res}    0.5666

95%{txt} confidence set for null hypothesis expression: {res}[−.1097, .2052]
{err}2005

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2589 {c |}{res}      2,471      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,471      100.00
{txt}{p 0 6 2}note: {bf:2633.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,448
                                                {txt}F(16, 47)         =  {res}     3.87
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0094
                                                {txt}Root MSE          =    {res} .45637

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}  .143474{col 56}{space 2} .0741697{col 67}{space 1}    1.93{col 76}{space 3}0.059{col 84}{space 4}-.0057362{col 97}{space 3} .2926842
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.1594009{col 56}{space 2} .1317921{col 67}{space 1}   -1.21{col 76}{space 3}0.233{col 84}{space 4}-.4245323{col 97}{space 3} .1057305
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0263382{col 56}{space 2} .0551815{col 67}{space 1}   -0.48{col 76}{space 3}0.635{col 84}{space 4}-.1373492{col 97}{space 3} .0846727
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0319753{col 56}{space 2} .0569106{col 67}{space 1}   -0.56{col 76}{space 3}0.577{col 84}{space 4}-.1464647{col 97}{space 3} .0825141
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0266406{col 56}{space 2} .0703637{col 67}{space 1}   -0.38{col 76}{space 3}0.707{col 84}{space 4}-.1681941{col 97}{space 3} .1149128
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0482585{col 56}{space 2} .0557228{col 67}{space 1}   -0.87{col 76}{space 3}0.391{col 84}{space 4}-.1603583{col 97}{space 3} .0638413
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0115236{col 56}{space 2} .0785924{col 67}{space 1}   -0.15{col 76}{space 3}0.884{col 84}{space 4}-.1696311{col 97}{space 3}  .146584
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0012125{col 56}{space 2} .0549192{col 67}{space 1}    0.02{col 76}{space 3}0.982{col 84}{space 4}-.1092708{col 97}{space 3} .1116957
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0124605{col 56}{space 2} .0221646{col 67}{space 1}    0.56{col 76}{space 3}0.577{col 84}{space 4}-.0321289{col 97}{space 3} .0570499
{txt}{space 39}age {c |}{col 44}{res}{space 2}   .00008{col 56}{space 2} .0008426{col 67}{space 1}    0.09{col 76}{space 3}0.925{col 84}{space 4}-.0016151{col 97}{space 3}  .001775
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0429998{col 56}{space 2} .0235549{col 67}{space 1}   -1.83{col 76}{space 3}0.074{col 84}{space 4}-.0903861{col 97}{space 3} .0043865
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0685211{col 56}{space 2} .0298839{col 67}{space 1}   -2.29{col 76}{space 3}0.026{col 84}{space 4}-.1286399{col 97}{space 3}-.0084024
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0386443{col 56}{space 2} .0382361{col 67}{space 1}   -1.01{col 76}{space 3}0.317{col 84}{space 4}-.1155654{col 97}{space 3} .0382768
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0238878{col 56}{space 2} .0452576{col 67}{space 1}   -0.53{col 76}{space 3}0.600{col 84}{space 4}-.1149344{col 97}{space 3} .0671588
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0260509{col 56}{space 2} .0412688{col 67}{space 1}    0.63{col 76}{space 3}0.531{col 84}{space 4}-.0569712{col 97}{space 3}  .109073
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0291706{col 56}{space 2} .0350925{col 67}{space 1}   -0.83{col 76}{space 3}0.410{col 84}{space 4}-.0997676{col 97}{space 3} .0414265
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2633  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3825262{col 56}{space 2} .0876736{col 67}{space 1}    4.36{col 76}{space 3}0.000{col 84}{space 4} .2061497{col 97}{space 3} .5589028
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2006}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,448    .2969771    .4570197          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    1.9344
{col 25}{txt}Prob>|t| = {res}    0.6567

95%{txt} confidence set for null hypothesis expression: {res}[−.6859, 1.292]
{err}2006

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2633 {c |}{res}      2,448      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,448      100.00
{txt}{p 0 6 2}note: {bf:2672.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,453
                                                {txt}F(16, 47)         =  {res}     1.89
                                                {txt}Prob > F          = {res}    0.0461
                                                {txt}R-squared         = {res}    0.0094
                                                {txt}Root MSE          =    {res} .45351

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .1053545{col 56}{space 2}  .110976{col 67}{space 1}    0.95{col 76}{space 3}0.347{col 84}{space 4}-.1179003{col 97}{space 3} .3286094
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.0596339{col 56}{space 2} .0769694{col 67}{space 1}   -0.77{col 76}{space 3}0.442{col 84}{space 4}-.2144763{col 97}{space 3} .0952085
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0232482{col 56}{space 2} .0439362{col 67}{space 1}    0.53{col 76}{space 3}0.599{col 84}{space 4}  -.06514{col 97}{space 3} .1116365
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0322965{col 56}{space 2} .0418998{col 67}{space 1}   -0.77{col 76}{space 3}0.445{col 84}{space 4}-.1165881{col 97}{space 3} .0519951
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} -.053443{col 56}{space 2} .0507366{col 67}{space 1}   -1.05{col 76}{space 3}0.298{col 84}{space 4} -.155512{col 97}{space 3} .0486259
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} -.010961{col 56}{space 2} .0465321{col 67}{space 1}   -0.24{col 76}{space 3}0.815{col 84}{space 4}-.1045716{col 97}{space 3} .0826496
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0177336{col 56}{space 2} .0544895{col 67}{space 1}   -0.33{col 76}{space 3}0.746{col 84}{space 4}-.1273524{col 97}{space 3} .0918851
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0097646{col 56}{space 2} .0470954{col 67}{space 1}    0.21{col 76}{space 3}0.837{col 84}{space 4}-.0849791{col 97}{space 3} .1045084
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0326515{col 56}{space 2} .0150781{col 67}{space 1}   -2.17{col 76}{space 3}0.035{col 84}{space 4}-.0629847{col 97}{space 3}-.0023182
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0000988{col 56}{space 2} .0007167{col 67}{space 1}   -0.14{col 76}{space 3}0.891{col 84}{space 4}-.0015406{col 97}{space 3} .0013431
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0174778{col 56}{space 2} .0235366{col 67}{space 1}   -0.74{col 76}{space 3}0.461{col 84}{space 4}-.0648272{col 97}{space 3} .0298717
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0860655{col 56}{space 2} .0352071{col 67}{space 1}   -2.44{col 76}{space 3}0.018{col 84}{space 4}-.1568931{col 97}{space 3}-.0152379
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0328851{col 56}{space 2} .0297835{col 67}{space 1}    1.10{col 76}{space 3}0.275{col 84}{space 4}-.0270315{col 97}{space 3} .0928017
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0101615{col 56}{space 2} .0317503{col 67}{space 1}   -0.32{col 76}{space 3}0.750{col 84}{space 4} -.074035{col 97}{space 3}  .053712
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0317035{col 56}{space 2} .0466909{col 67}{space 1}    0.68{col 76}{space 3}0.500{col 84}{space 4}-.0622264{col 97}{space 3} .1256334
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0149922{col 56}{space 2} .0352881{col 67}{space 1}    0.42{col 76}{space 3}0.673{col 84}{space 4}-.0559983{col 97}{space 3} .0859828
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2672  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3538159{col 56}{space 2} .0559046{col 67}{space 1}    6.33{col 76}{space 3}0.000{col 84}{space 4} .2413502{col 97}{space 3} .4662815
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2007}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,453    .2906645    .4541616          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    0.9493
{col 25}{txt}Prob>|t| = {res}    0.5155

95%{txt} confidence set for null hypothesis expression: {res}[−.5084, .5554]
{err}2007

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2672 {c |}{res}      2,453      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,453      100.00
{txt}{p 0 6 2}note: {bf:2750.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    17,659
                                                {txt}F(16, 49)         =  {res}     2.87
                                                {txt}Prob > F          = {res}    0.0023
                                                {txt}R-squared         = {res}    0.0031
                                                {txt}Root MSE          =    {res} .46158

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0072746{col 56}{space 2} .0131094{col 67}{space 1}   -0.55{col 76}{space 3}0.581{col 84}{space 4}-.0336189{col 97}{space 3} .0190697
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0183887{col 56}{space 2} .0595156{col 67}{space 1}    0.31{col 76}{space 3}0.759{col 84}{space 4}-.1012124{col 97}{space 3} .1379898
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0108684{col 56}{space 2} .0192061{col 67}{space 1}    0.57{col 76}{space 3}0.574{col 84}{space 4}-.0277278{col 97}{space 3} .0494646
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0046628{col 56}{space 2} .0191617{col 67}{space 1}    0.24{col 76}{space 3}0.809{col 84}{space 4} -.033844{col 97}{space 3} .0431697
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0085384{col 56}{space 2} .0235872{col 67}{space 1}    0.36{col 76}{space 3}0.719{col 84}{space 4}-.0388619{col 97}{space 3} .0559387
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} -.020949{col 56}{space 2} .0192352{col 67}{space 1}   -1.09{col 76}{space 3}0.281{col 84}{space 4}-.0596036{col 97}{space 3} .0177055
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}  .027065{col 56}{space 2} .0276742{col 67}{space 1}    0.98{col 76}{space 3}0.333{col 84}{space 4}-.0285483{col 97}{space 3} .0826783
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0273396{col 56}{space 2} .0282182{col 67}{space 1}   -0.97{col 76}{space 3}0.337{col 84}{space 4}-.0840461{col 97}{space 3}  .029367
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0009496{col 56}{space 2} .0078321{col 67}{space 1}    0.12{col 76}{space 3}0.904{col 84}{space 4}-.0147895{col 97}{space 3} .0166887
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0001442{col 56}{space 2} .0003392{col 67}{space 1}   -0.43{col 76}{space 3}0.673{col 84}{space 4}-.0008258{col 97}{space 3} .0005374
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0336601{col 56}{space 2} .0112987{col 67}{space 1}   -2.98{col 76}{space 3}0.004{col 84}{space 4}-.0563656{col 97}{space 3}-.0109545
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0338126{col 56}{space 2} .0174976{col 67}{space 1}   -1.93{col 76}{space 3}0.059{col 84}{space 4}-.0689752{col 97}{space 3} .0013501
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0468328{col 56}{space 2} .0170091{col 67}{space 1}    2.75{col 76}{space 3}0.008{col 84}{space 4} .0126517{col 97}{space 3} .0810138
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0054826{col 56}{space 2}  .013853{col 67}{space 1}   -0.40{col 76}{space 3}0.694{col 84}{space 4}-.0333213{col 97}{space 3} .0223561
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0082401{col 56}{space 2} .0196974{col 67}{space 1}    0.42{col 76}{space 3}0.678{col 84}{space 4}-.0313433{col 97}{space 3} .0478235
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0147305{col 56}{space 2} .0130724{col 67}{space 1}   -1.13{col 76}{space 3}0.265{col 84}{space 4}-.0410004{col 97}{space 3} .0115395
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2750  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3267413{col 56}{space 2} .0339421{col 67}{space 1}    9.63{col 76}{space 3}0.000{col 84}{space 4} .2585321{col 97}{space 3} .3949504
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2008}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     17,659    .3089643    .4620795          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.5549
{col 25}{txt}Prob>|t| = {res}    0.5475

95%{txt} confidence set for null hypothesis expression: {res}[−.4511, .3574]
{err}2008

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2750 {c |}{res}     17,659      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     17,659      100.00
{txt}{p 0 6 2}note: {bf:2782.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,464
                                                {txt}F(16, 46)         =  {res}    12.45
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0126
                                                {txt}Root MSE          =    {res} .45329

{txt}{ralign 108:(Std. err. adjusted for {res:47} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .1059861{col 56}{space 2} .0401779{col 67}{space 1}    2.64{col 76}{space 3}0.011{col 84}{space 4} .0251123{col 97}{space 3} .1868599
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.2724258{col 56}{space 2} .0729882{col 67}{space 1}   -3.73{col 76}{space 3}0.001{col 84}{space 4}-.4193434{col 97}{space 3}-.1255081
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0708447{col 56}{space 2} .0480022{col 67}{space 1}    1.48{col 76}{space 3}0.147{col 84}{space 4}-.0257787{col 97}{space 3}  .167468
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0674794{col 56}{space 2}  .038731{col 67}{space 1}    1.74{col 76}{space 3}0.088{col 84}{space 4} -.010482{col 97}{space 3} .1454409
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0744407{col 56}{space 2} .0517794{col 67}{space 1}    1.44{col 76}{space 3}0.157{col 84}{space 4}-.0297858{col 97}{space 3} .1786671
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0568018{col 56}{space 2} .0407525{col 67}{space 1}    1.39{col 76}{space 3}0.170{col 84}{space 4}-.0252287{col 97}{space 3} .1388322
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0680881{col 56}{space 2} .0577016{col 67}{space 1}    1.18{col 76}{space 3}0.244{col 84}{space 4}-.0480591{col 97}{space 3} .1842354
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .1159678{col 56}{space 2} .0369212{col 67}{space 1}    3.14{col 76}{space 3}0.003{col 84}{space 4} .0416493{col 97}{space 3} .1902863
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0155388{col 56}{space 2} .0276391{col 67}{space 1}    0.56{col 76}{space 3}0.577{col 84}{space 4}-.0400958{col 97}{space 3} .0711733
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0008087{col 56}{space 2} .0008141{col 67}{space 1}    0.99{col 76}{space 3}0.326{col 84}{space 4}-.0008301{col 97}{space 3} .0024475
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0347885{col 56}{space 2} .0188249{col 67}{space 1}   -1.85{col 76}{space 3}0.071{col 84}{space 4}-.0726811{col 97}{space 3} .0031041
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0388116{col 56}{space 2} .0319492{col 67}{space 1}   -1.21{col 76}{space 3}0.231{col 84}{space 4} -.103122{col 97}{space 3} .0254989
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0030465{col 56}{space 2} .0250177{col 67}{space 1}   -0.12{col 76}{space 3}0.904{col 84}{space 4}-.0534045{col 97}{space 3} .0473114
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0135243{col 56}{space 2} .0272095{col 67}{space 1}   -0.50{col 76}{space 3}0.622{col 84}{space 4}-.0682943{col 97}{space 3} .0412456
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0264638{col 56}{space 2} .0487266{col 67}{space 1}    0.54{col 76}{space 3}0.590{col 84}{space 4}-.0716178{col 97}{space 3} .1245455
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0555021{col 56}{space 2} .0418163{col 67}{space 1}   -1.33{col 76}{space 3}0.191{col 84}{space 4} -.139674{col 97}{space 3} .0286699
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2782  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2687436{col 56}{space 2} .0697331{col 67}{space 1}    3.85{col 76}{space 3}0.000{col 84}{space 4} .1283781{col 97}{space 3}  .409109
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2009}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,464    .2918019    .4546839          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(46) = {res}    2.6379
{col 25}{txt}Prob>|t| = {res}    0.4174

95%{txt} confidence set for null hypothesis expression: {res}[−.2669, .4527]
{err}2009

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2782 {c |}{res}      2,464      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,464      100.00
{txt}{p 0 6 2}note: {bf:2828.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,458
                                                {txt}F(16, 47)         =  {res}    22.72
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0159
                                                {txt}Root MSE          =    {res} .42856

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.1234423{col 56}{space 2} .0285902{col 67}{space 1}   -4.32{col 76}{space 3}0.000{col 84}{space 4}-.1809584{col 97}{space 3}-.0659262
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.2165078{col 56}{space 2} .1477854{col 67}{space 1}   -1.47{col 76}{space 3}0.150{col 84}{space 4}-.5138137{col 97}{space 3} .0807981
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0060509{col 56}{space 2} .0383313{col 67}{space 1}    0.16{col 76}{space 3}0.875{col 84}{space 4}-.0710617{col 97}{space 3} .0831634
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0175785{col 56}{space 2} .0382841{col 67}{space 1}    0.46{col 76}{space 3}0.648{col 84}{space 4}-.0594392{col 97}{space 3} .0945962
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0086396{col 56}{space 2} .0359504{col 67}{space 1}    0.24{col 76}{space 3}0.811{col 84}{space 4}-.0636832{col 97}{space 3} .0809624
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0249565{col 56}{space 2} .0375196{col 67}{space 1}    0.67{col 76}{space 3}0.509{col 84}{space 4}-.0505231{col 97}{space 3} .1004362
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0906712{col 56}{space 2} .0458277{col 67}{space 1}    1.98{col 76}{space 3}0.054{col 84}{space 4}-.0015222{col 97}{space 3} .1828647
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0399679{col 56}{space 2} .0325426{col 67}{space 1}    1.23{col 76}{space 3}0.225{col 84}{space 4}-.0254993{col 97}{space 3} .1054351
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0023514{col 56}{space 2} .0139995{col 67}{space 1}    0.17{col 76}{space 3}0.867{col 84}{space 4}-.0258118{col 97}{space 3} .0305147
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0020488{col 56}{space 2} .0007197{col 67}{space 1}    2.85{col 76}{space 3}0.007{col 84}{space 4} .0006009{col 97}{space 3} .0034966
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0014408{col 56}{space 2} .0210412{col 67}{space 1}    0.07{col 76}{space 3}0.946{col 84}{space 4}-.0408885{col 97}{space 3} .0437702
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} -.031124{col 56}{space 2} .0263194{col 67}{space 1}   -1.18{col 76}{space 3}0.243{col 84}{space 4}-.0840718{col 97}{space 3} .0218237
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0209048{col 56}{space 2}  .032997{col 67}{space 1}    0.63{col 76}{space 3}0.529{col 84}{space 4}-.0454766{col 97}{space 3} .0872861
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0025548{col 56}{space 2} .0315675{col 67}{space 1}    0.08{col 76}{space 3}0.936{col 84}{space 4}-.0609509{col 97}{space 3} .0660604
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0034976{col 56}{space 2} .0444146{col 67}{space 1}    0.08{col 76}{space 3}0.938{col 84}{space 4} -.085853{col 97}{space 3} .0928482
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0517909{col 56}{space 2} .0267569{col 67}{space 1}    1.94{col 76}{space 3}0.059{col 84}{space 4} -.002037{col 97}{space 3} .1056189
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2828  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1861586{col 56}{space 2} .0710718{col 67}{space 1}    2.62{col 76}{space 3}0.012{col 84}{space 4} .0431806{col 97}{space 3} .3291366
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2010}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,458    .2457282    .4306057          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}   -4.3176
{col 25}{txt}Prob>|t| = {res}    0.2302

95%{txt} confidence set for null hypothesis expression: {res}[−.5504, .3064]
{err}2010

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2828 {c |}{res}      2,458      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,458      100.00
{txt}{p 0 6 2}note: {bf:2859.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,462
                                                {txt}F(16, 47)         =  {res}     5.13
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0167
                                                {txt}Root MSE          =    {res}  .4087

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .2023116{col 56}{space 2} .0734106{col 67}{space 1}    2.76{col 76}{space 3}0.008{col 84}{space 4} .0546284{col 97}{space 3} .3499948
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}-.1041807{col 56}{space 2} .1350152{col 67}{space 1}   -0.77{col 76}{space 3}0.444{col 84}{space 4}-.3757962{col 97}{space 3} .1674348
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0538387{col 56}{space 2} .0420634{col 67}{space 1}   -1.28{col 76}{space 3}0.207{col 84}{space 4}-.1384593{col 97}{space 3} .0307818
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0758869{col 56}{space 2} .0491073{col 67}{space 1}   -1.55{col 76}{space 3}0.129{col 84}{space 4}-.1746781{col 97}{space 3} .0229042
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0735116{col 56}{space 2} .0542169{col 67}{space 1}   -1.36{col 76}{space 3}0.182{col 84}{space 4} -.182582{col 97}{space 3} .0355588
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0538039{col 56}{space 2} .0443106{col 67}{space 1}   -1.21{col 76}{space 3}0.231{col 84}{space 4}-.1429453{col 97}{space 3} .0353374
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.1044446{col 56}{space 2}  .064029{col 67}{space 1}   -1.63{col 76}{space 3}0.110{col 84}{space 4}-.2332544{col 97}{space 3} .0243652
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0527444{col 56}{space 2} .0429732{col 67}{space 1}   -1.23{col 76}{space 3}0.226{col 84}{space 4}-.1391953{col 97}{space 3} .0337064
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0243024{col 56}{space 2} .0152943{col 67}{space 1}    1.59{col 76}{space 3}0.119{col 84}{space 4}-.0064658{col 97}{space 3} .0550706
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0008528{col 56}{space 2} .0007382{col 67}{space 1}    1.16{col 76}{space 3}0.254{col 84}{space 4}-.0006323{col 97}{space 3} .0023379
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0358499{col 56}{space 2} .0215493{col 67}{space 1}   -1.66{col 76}{space 3}0.103{col 84}{space 4}-.0792015{col 97}{space 3} .0075016
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0508147{col 56}{space 2} .0265263{col 67}{space 1}   -1.92{col 76}{space 3}0.062{col 84}{space 4}-.1041788{col 97}{space 3} .0025494
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0082207{col 56}{space 2} .0296674{col 67}{space 1}    0.28{col 76}{space 3}0.783{col 84}{space 4}-.0514624{col 97}{space 3} .0679038
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0122034{col 56}{space 2} .0297992{col 67}{space 1}    0.41{col 76}{space 3}0.684{col 84}{space 4}-.0477448{col 97}{space 3} .0721516
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0118803{col 56}{space 2} .0544341{col 67}{space 1}    0.22{col 76}{space 3}0.828{col 84}{space 4} -.097627{col 97}{space 3} .1213877
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0556238{col 56}{space 2} .0350633{col 67}{space 1}    1.59{col 76}{space 3}0.119{col 84}{space 4}-.0149144{col 97}{space 3}  .126162
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2859  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2494979{col 56}{space 2} .0598019{col 67}{space 1}    4.17{col 76}{space 3}0.000{col 84}{space 4}  .129192{col 97}{space 3} .3698039
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2011}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,462     .214866    .4108128          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    2.7559
{col 25}{txt}Prob>|t| = {res}    0.1251

95%{txt} confidence set for null hypothesis expression: {res}[−.04128, .6695]
{err}2011

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2859 {c |}{res}      2,462      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,462      100.00
{txt}{p 0 6 2}note: {bf:2927.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,447
                                                {txt}F(16, 47)         =  {res}     1.47
                                                {txt}Prob > F          = {res}    0.1537
                                                {txt}R-squared         = {res}    0.0177
                                                {txt}Root MSE          =    {res} .45733

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0451954{col 56}{space 2} .0264869{col 67}{space 1}    1.71{col 76}{space 3}0.095{col 84}{space 4}-.0080894{col 97}{space 3} .0984802
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .5561724{col 56}{space 2} .2479074{col 67}{space 1}    2.24{col 76}{space 3}0.030{col 84}{space 4} .0574471{col 97}{space 3} 1.054898
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0110233{col 56}{space 2} .0594798{col 67}{space 1}    0.19{col 76}{space 3}0.854{col 84}{space 4}-.1086346{col 97}{space 3} .1306812
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0251883{col 56}{space 2} .0615923{col 67}{space 1}    0.41{col 76}{space 3}0.684{col 84}{space 4}-.0987195{col 97}{space 3} .1490961
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0355961{col 56}{space 2} .0631878{col 67}{space 1}    0.56{col 76}{space 3}0.576{col 84}{space 4}-.0915213{col 97}{space 3} .1627135
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0119253{col 56}{space 2} .0622465{col 67}{space 1}    0.19{col 76}{space 3}0.849{col 84}{space 4}-.1132985{col 97}{space 3}  .137149
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0139577{col 56}{space 2} .0782224{col 67}{space 1}   -0.18{col 76}{space 3}0.859{col 84}{space 4} -.171321{col 97}{space 3} .1434055
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0367276{col 56}{space 2} .0769077{col 67}{space 1}   -0.48{col 76}{space 3}0.635{col 84}{space 4}-.1914459{col 97}{space 3} .1179908
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} -.015453{col 56}{space 2} .0160103{col 67}{space 1}   -0.97{col 76}{space 3}0.339{col 84}{space 4}-.0476615{col 97}{space 3} .0167555
{txt}{space 39}age {c |}{col 44}{res}{space 2}  .001081{col 56}{space 2} .0006045{col 67}{space 1}    1.79{col 76}{space 3}0.080{col 84}{space 4}-.0001352{col 97}{space 3} .0022971
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0011619{col 56}{space 2} .0258925{col 67}{space 1}   -0.04{col 76}{space 3}0.964{col 84}{space 4}-.0532509{col 97}{space 3} .0509271
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0121563{col 56}{space 2} .0291126{col 67}{space 1}   -0.42{col 76}{space 3}0.678{col 84}{space 4}-.0707233{col 97}{space 3} .0464106
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0009183{col 56}{space 2} .0251997{col 67}{space 1}    0.04{col 76}{space 3}0.971{col 84}{space 4} -.049777{col 97}{space 3} .0516137
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0126358{col 56}{space 2}  .029566{col 67}{space 1}    0.43{col 76}{space 3}0.671{col 84}{space 4}-.0468433{col 97}{space 3} .0721148
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0310339{col 56}{space 2}  .054844{col 67}{space 1}   -0.57{col 76}{space 3}0.574{col 84}{space 4}-.1413659{col 97}{space 3} .0792981
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0309712{col 56}{space 2} .0496942{col 67}{space 1}    0.62{col 76}{space 3}0.536{col 84}{space 4}-.0690007{col 97}{space 3}  .130943
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2927  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1029806{col 56}{space 2} .0978217{col 67}{space 1}    1.05{col 76}{space 3}0.298{col 84}{space 4}-.0938112{col 97}{space 3} .2997724
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2012}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,447    .3036371    .4599218          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    1.7063
{col 25}{txt}Prob>|t| = {res}    0.2072

95%{txt} confidence set for null hypothesis expression: {res}[−2.119, .7564]
{err}2012

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2927 {c |}{res}      2,447      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,447      100.00
{txt}{p 0 6 2}note: {bf:2976.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,463
                                                {txt}F(16, 47)         =  {res}     6.25
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0285
                                                {txt}Root MSE          =    {res} .36107

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0088872{col 56}{space 2} .0376585{col 67}{space 1}   -0.24{col 76}{space 3}0.814{col 84}{space 4}-.0846464{col 97}{space 3}  .066872
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .3243572{col 56}{space 2} .1874875{col 67}{space 1}    1.73{col 76}{space 3}0.090{col 84}{space 4} -.052819{col 97}{space 3} .7015333
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0242062{col 56}{space 2} .0470612{col 67}{space 1}   -0.51{col 76}{space 3}0.609{col 84}{space 4}-.1188811{col 97}{space 3} .0704687
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0115844{col 56}{space 2} .0433215{col 67}{space 1}   -0.27{col 76}{space 3}0.790{col 84}{space 4} -.098736{col 97}{space 3} .0755672
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0257321{col 56}{space 2} .0504574{col 67}{space 1}   -0.51{col 76}{space 3}0.612{col 84}{space 4}-.1272392{col 97}{space 3} .0757751
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0248559{col 56}{space 2} .0451731{col 67}{space 1}   -0.55{col 76}{space 3}0.585{col 84}{space 4}-.1157325{col 97}{space 3} .0660208
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0043062{col 56}{space 2} .0593144{col 67}{space 1}   -0.07{col 76}{space 3}0.942{col 84}{space 4}-.1236314{col 97}{space 3}  .115019
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} -.006815{col 56}{space 2} .0433699{col 67}{space 1}   -0.16{col 76}{space 3}0.876{col 84}{space 4} -.094064{col 97}{space 3}  .080434
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0185889{col 56}{space 2} .0120301{col 67}{space 1}   -1.55{col 76}{space 3}0.129{col 84}{space 4}-.0427903{col 97}{space 3} .0056125
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0006404{col 56}{space 2} .0006389{col 67}{space 1}    1.00{col 76}{space 3}0.321{col 84}{space 4} -.000645{col 97}{space 3} .0019257
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0481877{col 56}{space 2} .0166685{col 67}{space 1}   -2.89{col 76}{space 3}0.006{col 84}{space 4}-.0817204{col 97}{space 3}-.0146551
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0294527{col 56}{space 2} .0221338{col 67}{space 1}   -1.33{col 76}{space 3}0.190{col 84}{space 4}-.0739802{col 97}{space 3} .0150748
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0587032{col 56}{space 2} .0175119{col 67}{space 1}   -3.35{col 76}{space 3}0.002{col 84}{space 4}-.0939326{col 97}{space 3}-.0234739
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0417111{col 56}{space 2} .0238509{col 67}{space 1}    1.75{col 76}{space 3}0.087{col 84}{space 4}-.0062708{col 97}{space 3} .0896929
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0077513{col 56}{space 2} .0429198{col 67}{space 1}    0.18{col 76}{space 3}0.857{col 84}{space 4}-.0785923{col 97}{space 3} .0940948
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0195524{col 56}{space 2} .0315027{col 67}{space 1}    0.62{col 76}{space 3}0.538{col 84}{space 4}-.0438228{col 97}{space 3} .0829277
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2976  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  .099433{col 56}{space 2} .0695655{col 67}{space 1}    1.43{col 76}{space 3}0.160{col 84}{space 4}-.0405147{col 97}{space 3} .2393807
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2013}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,463    .1583435    .3651369          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}   -0.2360
{col 25}{txt}Prob>|t| = {res}    0.8358

95%{txt} confidence set for null hypothesis expression: {res}[−.5484, .3579]
{err}2013

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2976 {c |}{res}      2,463      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,463      100.00
{txt}{p 0 6 2}note: {bf:3011.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,462
                                                {txt}F(16, 46)         =  {res}     8.04
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0309
                                                {txt}Root MSE          =    {res} .30666

{txt}{ralign 108:(Std. err. adjusted for {res:47} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0332616{col 56}{space 2} .0584943{col 67}{space 1}    0.57{col 76}{space 3}0.572{col 84}{space 4}-.0844813{col 97}{space 3} .1510045
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}  .290487{col 56}{space 2} .1194866{col 67}{space 1}    2.43{col 76}{space 3}0.019{col 84}{space 4}  .049973{col 97}{space 3}  .531001
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0137343{col 56}{space 2} .0305959{col 67}{space 1}   -0.45{col 76}{space 3}0.656{col 84}{space 4}-.0753207{col 97}{space 3} .0478521
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0105292{col 56}{space 2} .0279559{col 67}{space 1}   -0.38{col 76}{space 3}0.708{col 84}{space 4}-.0668016{col 97}{space 3} .0457431
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0001357{col 56}{space 2} .0288201{col 67}{space 1}   -0.00{col 76}{space 3}0.996{col 84}{space 4}-.0581475{col 97}{space 3} .0578761
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0032848{col 56}{space 2} .0270376{col 67}{space 1}   -0.12{col 76}{space 3}0.904{col 84}{space 4}-.0577086{col 97}{space 3} .0511391
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0022521{col 56}{space 2} .0285322{col 67}{space 1}    0.08{col 76}{space 3}0.937{col 84}{space 4}-.0551803{col 97}{space 3} .0596844
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0047762{col 56}{space 2} .0315704{col 67}{space 1}    0.15{col 76}{space 3}0.880{col 84}{space 4}-.0587717{col 97}{space 3} .0683242
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} -.032774{col 56}{space 2} .0133743{col 67}{space 1}   -2.45{col 76}{space 3}0.018{col 84}{space 4}-.0596951{col 97}{space 3}-.0058529
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0014098{col 56}{space 2} .0005717{col 67}{space 1}    2.47{col 76}{space 3}0.017{col 84}{space 4} .0002591{col 97}{space 3} .0025605
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.038197{col 56}{space 2} .0131848{col 67}{space 1}   -2.90{col 76}{space 3}0.006{col 84}{space 4}-.0647365{col 97}{space 3}-.0116575
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0176857{col 56}{space 2} .0177408{col 67}{space 1}   -1.00{col 76}{space 3}0.324{col 84}{space 4}-.0533961{col 97}{space 3} .0180248
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0070504{col 56}{space 2} .0145927{col 67}{space 1}   -0.48{col 76}{space 3}0.631{col 84}{space 4}-.0364239{col 97}{space 3} .0223231
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0304667{col 56}{space 2}  .020199{col 67}{space 1}    1.51{col 76}{space 3}0.138{col 84}{space 4}-.0101919{col 97}{space 3} .0711252
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0307516{col 56}{space 2} .0193729{col 67}{space 1}   -1.59{col 76}{space 3}0.119{col 84}{space 4}-.0697472{col 97}{space 3}  .008244
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0239303{col 56}{space 2} .0288218{col 67}{space 1}    0.83{col 76}{space 3}0.411{col 84}{space 4}-.0340849{col 97}{space 3} .0819455
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3011  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  .000732{col 56}{space 2} .0430941{col 67}{space 1}    0.02{col 76}{space 3}0.987{col 84}{space 4}-.0860118{col 97}{space 3} .0874759
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2014}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,462    .1080422    .3104968          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(46) = {res}    0.5686
{col 25}{txt}Prob>|t| = {res}    0.5926

95%{txt} confidence set for null hypothesis expression: {res}[−.1044, .7223]
{err}2014

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3011 {c |}{res}      2,462      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,462      100.00
{txt}{p 0 6 2}note: {bf:3050.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,466
                                                {txt}F(16, 45)         =  {res}    16.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0642
                                                {txt}Root MSE          =    {res} .32578

{txt}{ralign 108:(Std. err. adjusted for {res:46} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0110007{col 56}{space 2} .0459257{col 67}{space 1}    0.24{col 76}{space 3}0.812{col 84}{space 4}-.0814983{col 97}{space 3} .1034998
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}  .408806{col 56}{space 2} .1561468{col 67}{space 1}    2.62{col 76}{space 3}0.012{col 84}{space 4} .0943101{col 97}{space 3} .7233019
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0089069{col 56}{space 2} .0414243{col 67}{space 1}    0.22{col 76}{space 3}0.831{col 84}{space 4} -.074526{col 97}{space 3} .0923398
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0314081{col 56}{space 2} .0343024{col 67}{space 1}   -0.92{col 76}{space 3}0.365{col 84}{space 4}-.1004966{col 97}{space 3} .0376804
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0030682{col 56}{space 2} .0388196{col 67}{space 1}   -0.08{col 76}{space 3}0.937{col 84}{space 4} -.081255{col 97}{space 3} .0751186
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} -.040228{col 56}{space 2} .0328277{col 67}{space 1}   -1.23{col 76}{space 3}0.227{col 84}{space 4}-.1063464{col 97}{space 3} .0258904
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0000235{col 56}{space 2} .0383786{col 67}{space 1}   -0.00{col 76}{space 3}1.000{col 84}{space 4}-.0773221{col 97}{space 3}  .077275
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0245708{col 56}{space 2} .0334518{col 67}{space 1}   -0.73{col 76}{space 3}0.466{col 84}{space 4}-.0919462{col 97}{space 3} .0428046
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0121532{col 56}{space 2} .0124307{col 67}{space 1}   -0.98{col 76}{space 3}0.333{col 84}{space 4}-.0371899{col 97}{space 3} .0128835
{txt}{space 39}age {c |}{col 44}{res}{space 2}  .002712{col 56}{space 2} .0006816{col 67}{space 1}    3.98{col 76}{space 3}0.000{col 84}{space 4} .0013392{col 97}{space 3} .0040849
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0165413{col 56}{space 2} .0190554{col 67}{space 1}   -0.87{col 76}{space 3}0.390{col 84}{space 4}-.0549209{col 97}{space 3} .0218383
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0297008{col 56}{space 2} .0235685{col 67}{space 1}   -1.26{col 76}{space 3}0.214{col 84}{space 4}-.0771702{col 97}{space 3} .0177686
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} -.021401{col 56}{space 2}  .015371{col 67}{space 1}   -1.39{col 76}{space 3}0.171{col 84}{space 4}-.0523598{col 97}{space 3} .0095579
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}    .0522{col 56}{space 2} .0276547{col 67}{space 1}    1.89{col 76}{space 3}0.066{col 84}{space 4}-.0034995{col 97}{space 3} .1078995
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0067449{col 56}{space 2} .0214601{col 67}{space 1}    0.31{col 76}{space 3}0.755{col 84}{space 4}-.0364779{col 97}{space 3} .0499677
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0500462{col 56}{space 2} .0365809{col 67}{space 1}    1.37{col 76}{space 3}0.178{col 84}{space 4}-.0236316{col 97}{space 3} .1237239
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3050  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}-.0761018{col 56}{space 2} .0493263{col 67}{space 1}   -1.54{col 76}{space 3}0.130{col 84}{space 4}-.1754502{col 97}{space 3} .0232465
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2015}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,466    .1293593    .3356652          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(45) = {res}    0.2395
{col 25}{txt}Prob>|t| = {res}    0.8078

95%{txt} confidence set for null hypothesis expression: {res}[−.1668, .09859]
{err}2015

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3050 {c |}{res}      2,466      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,466      100.00
{txt}{p 0 6 2}note: {bf:3124.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,480
                                                {txt}F(16, 47)         =  {res}    32.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0562
                                                {txt}Root MSE          =    {res} .37603

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0400779{col 56}{space 2} .0054391{col 67}{space 1}    7.37{col 76}{space 3}0.000{col 84}{space 4} .0291359{col 97}{space 3} .0510199
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .2274724{col 56}{space 2} .1806542{col 67}{space 1}    1.26{col 76}{space 3}0.214{col 84}{space 4} -.135957{col 97}{space 3} .5909018
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0336667{col 56}{space 2} .0405731{col 67}{space 1}    0.83{col 76}{space 3}0.411{col 84}{space 4}-.0479558{col 97}{space 3} .1152893
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0294301{col 56}{space 2} .0386429{col 67}{space 1}   -0.76{col 76}{space 3}0.450{col 84}{space 4}-.1071696{col 97}{space 3} .0483095
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0433815{col 56}{space 2} .0386698{col 67}{space 1}    1.12{col 76}{space 3}0.268{col 84}{space 4}-.0344122{col 97}{space 3} .1211751
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0397599{col 56}{space 2} .0416101{col 67}{space 1}    0.96{col 76}{space 3}0.344{col 84}{space 4}-.0439489{col 97}{space 3} .1234686
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} -.035679{col 56}{space 2} .0479183{col 67}{space 1}   -0.74{col 76}{space 3}0.460{col 84}{space 4}-.1320782{col 97}{space 3} .0607202
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0402685{col 56}{space 2} .0470502{col 67}{space 1}   -0.86{col 76}{space 3}0.396{col 84}{space 4}-.1349212{col 97}{space 3} .0543843
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0152118{col 56}{space 2} .0147616{col 67}{space 1}   -1.03{col 76}{space 3}0.308{col 84}{space 4}-.0449083{col 97}{space 3} .0144847
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0029217{col 56}{space 2}  .001005{col 67}{space 1}    2.91{col 76}{space 3}0.006{col 84}{space 4}    .0009{col 97}{space 3} .0049435
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0092782{col 56}{space 2} .0210122{col 67}{space 1}   -0.44{col 76}{space 3}0.661{col 84}{space 4}-.0515493{col 97}{space 3} .0329928
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0156277{col 56}{space 2} .0252434{col 67}{space 1}   -0.62{col 76}{space 3}0.539{col 84}{space 4}-.0664108{col 97}{space 3} .0351554
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0459905{col 56}{space 2} .0213868{col 67}{space 1}   -2.15{col 76}{space 3}0.037{col 84}{space 4}-.0890152{col 97}{space 3}-.0029657
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}  .049624{col 56}{space 2} .0281799{col 67}{space 1}    1.76{col 76}{space 3}0.085{col 84}{space 4}-.0070666{col 97}{space 3} .1063145
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .1146532{col 56}{space 2}  .053897{col 67}{space 1}    2.13{col 76}{space 3}0.039{col 84}{space 4} .0062264{col 97}{space 3} .2230799
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0572425{col 56}{space 2} .0365853{col 67}{space 1}    1.56{col 76}{space 3}0.124{col 84}{space 4}-.0163576{col 97}{space 3} .1308427
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3124  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}-.0285467{col 56}{space 2} .0801297{col 67}{space 1}   -0.36{col 76}{space 3}0.723{col 84}{space 4}-.1897469{col 97}{space 3} .1326535
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2016}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,480    .1818548    .3858026          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    7.3685
{col 25}{txt}Prob>|t| = {res}    0.4024

95%{txt} confidence set for null hypothesis expression: {res}[−.2094, .386]
{err}2016

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3124 {c |}{res}      2,480      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,480      100.00
{txt}{p 0 6 2}note: {bf:3164.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,362
                                                {txt}F(16, 47)         =  {res}     7.08
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0664
                                                {txt}Root MSE          =    {res} .40014

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}  .216167{col 56}{space 2} .1159045{col 67}{space 1}    1.87{col 76}{space 3}0.068{col 84}{space 4}-.0170028{col 97}{space 3} .4493369
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .4210518{col 56}{space 2} .1878302{col 67}{space 1}    2.24{col 76}{space 3}0.030{col 84}{space 4} .0431862{col 97}{space 3} .7989173
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0245817{col 56}{space 2} .0412047{col 67}{space 1}   -0.60{col 76}{space 3}0.554{col 84}{space 4}-.1074749{col 97}{space 3} .0583115
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0567556{col 56}{space 2} .0342421{col 67}{space 1}   -1.66{col 76}{space 3}0.104{col 84}{space 4}-.1256417{col 97}{space 3} .0121306
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}  .003234{col 56}{space 2}  .044461{col 67}{space 1}    0.07{col 76}{space 3}0.942{col 84}{space 4}  -.08621{col 97}{space 3}  .092678
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0215854{col 56}{space 2} .0382702{col 67}{space 1}   -0.56{col 76}{space 3}0.575{col 84}{space 4}-.0985751{col 97}{space 3} .0554044
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0078736{col 56}{space 2} .0624333{col 67}{space 1}    0.13{col 76}{space 3}0.900{col 84}{space 4}-.1177261{col 97}{space 3} .1334733
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0734171{col 56}{space 2} .0437015{col 67}{space 1}   -1.68{col 76}{space 3}0.100{col 84}{space 4}-.1613332{col 97}{space 3}  .014499
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0167041{col 56}{space 2} .0199803{col 67}{space 1}   -0.84{col 76}{space 3}0.407{col 84}{space 4}-.0568992{col 97}{space 3} .0234911
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0050802{col 56}{space 2}  .000918{col 67}{space 1}    5.53{col 76}{space 3}0.000{col 84}{space 4} .0032334{col 97}{space 3} .0069271
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0092117{col 56}{space 2} .0248924{col 67}{space 1}    0.37{col 76}{space 3}0.713{col 84}{space 4}-.0408653{col 97}{space 3} .0592887
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0443287{col 56}{space 2} .0288904{col 67}{space 1}    1.53{col 76}{space 3}0.132{col 84}{space 4}-.0137913{col 97}{space 3} .1024488
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0271425{col 56}{space 2} .0177608{col 67}{space 1}    1.53{col 76}{space 3}0.133{col 84}{space 4}-.0085877{col 97}{space 3} .0628727
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} -.001633{col 56}{space 2} .0297707{col 67}{space 1}   -0.05{col 76}{space 3}0.956{col 84}{space 4}-.0615239{col 97}{space 3} .0582578
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0920012{col 56}{space 2} .0303315{col 67}{space 1}    3.03{col 76}{space 3}0.004{col 84}{space 4} .0309821{col 97}{space 3} .1530204
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}  .141986{col 56}{space 2} .0407546{col 67}{space 1}    3.48{col 76}{space 3}0.001{col 84}{space 4} .0599983{col 97}{space 3} .2239736
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3164  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  -.14998{col 56}{space 2} .0742602{col 67}{space 1}   -2.02{col 76}{space 3}0.049{col 84}{space 4}-.2993723{col 97}{space 3}-.0005877
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2017}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,362    .2176122    .4127096          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(47) = {res}    1.8650
{col 25}{txt}Prob>|t| = {res}    0.3293

95%{txt} confidence set for null hypothesis expression: {res}[−.4785, .6235] ∪ [1.05, 1.883]
{err}2017

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3164 {c |}{res}      2,362      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,362      100.00
{txt}{p 0 6 2}note: {bf:3203.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,454
                                                {txt}F(16, 46)         =  {res}    12.96
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0556
                                                {txt}Root MSE          =    {res} .35126

{txt}{ralign 108:(Std. err. adjusted for {res:47} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0053587{col 56}{space 2} .0030369{col 67}{space 1}   -1.76{col 76}{space 3}0.084{col 84}{space 4}-.0114716{col 97}{space 3} .0007542
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .3581642{col 56}{space 2} .1520795{col 67}{space 1}    2.36{col 76}{space 3}0.023{col 84}{space 4}  .052044{col 97}{space 3} .6642844
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0891093{col 56}{space 2} .0336209{col 67}{space 1}   -2.65{col 76}{space 3}0.011{col 84}{space 4}-.1567848{col 97}{space 3}-.0214339
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0558638{col 56}{space 2} .0353905{col 67}{space 1}   -1.58{col 76}{space 3}0.121{col 84}{space 4}-.1271013{col 97}{space 3} .0153736
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0470589{col 56}{space 2} .0417772{col 67}{space 1}   -1.13{col 76}{space 3}0.266{col 84}{space 4}-.1311522{col 97}{space 3} .0370343
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} -.075272{col 56}{space 2} .0332001{col 67}{space 1}   -2.27{col 76}{space 3}0.028{col 84}{space 4}-.1421004{col 97}{space 3}-.0084436
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0501614{col 56}{space 2} .0432631{col 67}{space 1}   -1.16{col 76}{space 3}0.252{col 84}{space 4}-.1372455{col 97}{space 3} .0369228
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0443949{col 56}{space 2} .0448814{col 67}{space 1}   -0.99{col 76}{space 3}0.328{col 84}{space 4}-.1347365{col 97}{space 3} .0459467
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0083066{col 56}{space 2} .0130514{col 67}{space 1}   -0.64{col 76}{space 3}0.528{col 84}{space 4}-.0345776{col 97}{space 3} .0179645
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0027906{col 56}{space 2} .0008438{col 67}{space 1}    3.31{col 76}{space 3}0.002{col 84}{space 4}  .001092{col 97}{space 3} .0044892
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.046556{col 56}{space 2}  .014542{col 67}{space 1}   -3.20{col 76}{space 3}0.002{col 84}{space 4}-.0758275{col 97}{space 3}-.0172845
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0358313{col 56}{space 2} .0151913{col 67}{space 1}   -2.36{col 76}{space 3}0.023{col 84}{space 4}-.0664099{col 97}{space 3}-.0052527
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0181703{col 56}{space 2} .0202154{col 67}{space 1}   -0.90{col 76}{space 3}0.373{col 84}{space 4}-.0588619{col 97}{space 3} .0225212
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0267743{col 56}{space 2} .0256523{col 67}{space 1}    1.04{col 76}{space 3}0.302{col 84}{space 4}-.0248611{col 97}{space 3} .0784097
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}  .029837{col 56}{space 2} .0335096{col 67}{space 1}    0.89{col 76}{space 3}0.378{col 84}{space 4}-.0376143{col 97}{space 3} .0972884
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0387223{col 56}{space 2} .0455478{col 67}{space 1}    0.85{col 76}{space 3}0.400{col 84}{space 4}-.0529606{col 97}{space 3} .1304052
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3203  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .0017351{col 56}{space 2} .0570562{col 67}{space 1}    0.03{col 76}{space 3}0.976{col 84}{space 4}-.1131131{col 97}{space 3} .1165832
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2018}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,454    .1532192    .3602721          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(46) = {res}   -1.7645
{col 25}{txt}Prob>|t| = {res}    0.3584

95%{txt} confidence set for null hypothesis expression: {res}[−.09366, .1752]
{err}2018

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3203 {c |}{res}      2,454      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,454      100.00

{txt}Linear regression                               Number of obs     = {res}    21,689
                                                {txt}F(18, 49)         =  {res}    29.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0197
                                                {txt}Root MSE          =    {res} .39314

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0152906{col 56}{space 2} .0128041{col 67}{space 1}   -1.19{col 76}{space 3}0.238{col 84}{space 4}-.0410214{col 97}{space 3} .0104401
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0313346{col 56}{space 2} .0578391{col 67}{space 1}    0.54{col 76}{space 3}0.590{col 84}{space 4}-.0848974{col 97}{space 3} .1475666
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0201779{col 56}{space 2} .0157437{col 67}{space 1}    1.28{col 76}{space 3}0.206{col 84}{space 4}-.0114602{col 97}{space 3} .0518161
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0019449{col 56}{space 2} .0174486{col 67}{space 1}    0.11{col 76}{space 3}0.912{col 84}{space 4}-.0331193{col 97}{space 3} .0370091
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0138418{col 56}{space 2} .0186949{col 67}{space 1}    0.74{col 76}{space 3}0.463{col 84}{space 4}-.0237269{col 97}{space 3} .0514106
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}  .013259{col 56}{space 2} .0185602{col 67}{space 1}    0.71{col 76}{space 3}0.478{col 84}{space 4}-.0240391{col 97}{space 3} .0505571
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0087127{col 56}{space 2}  .025863{col 67}{space 1}    0.34{col 76}{space 3}0.738{col 84}{space 4} -.043261{col 97}{space 3} .0606864
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0247771{col 56}{space 2} .0185948{col 67}{space 1}    1.33{col 76}{space 3}0.189{col 84}{space 4}-.0125904{col 97}{space 3} .0621447
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0032257{col 56}{space 2} .0046263{col 67}{space 1}   -0.70{col 76}{space 3}0.489{col 84}{space 4}-.0125225{col 97}{space 3} .0060711
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0015198{col 56}{space 2} .0002934{col 67}{space 1}    5.18{col 76}{space 3}0.000{col 84}{space 4} .0009301{col 97}{space 3} .0021094
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0383561{col 56}{space 2} .0090414{col 67}{space 1}   -4.24{col 76}{space 3}0.000{col 84}{space 4}-.0565255{col 97}{space 3}-.0201868
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0494013{col 56}{space 2} .0087087{col 67}{space 1}   -5.67{col 76}{space 3}0.000{col 84}{space 4}-.0669022{col 97}{space 3}-.0319004
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0382707{col 56}{space 2} .0092822{col 67}{space 1}    4.12{col 76}{space 3}0.000{col 84}{space 4} .0196173{col 97}{space 3}  .056924
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0406064{col 56}{space 2} .0127696{col 67}{space 1}    3.18{col 76}{space 3}0.003{col 84}{space 4}  .014945{col 97}{space 3} .0662678
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0130965{col 56}{space 2} .0134927{col 67}{space 1}    0.97{col 76}{space 3}0.336{col 84}{space 4}-.0140182{col 97}{space 3} .0402111
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0087802{col 56}{space 2} .0110862{col 67}{space 1}   -0.79{col 76}{space 3}0.432{col 84}{space 4}-.0310588{col 97}{space 3} .0134984
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3240  {c |}{col 44}{res}{space 2}   .03192{col 56}{space 2} .0108372{col 67}{space 1}    2.95{col 76}{space 3}0.005{col 84}{space 4} .0101419{col 97}{space 3} .0536981
{txt}{space 37}3242  {c |}{col 44}{res}{space 2}-.0183186{col 56}{space 2}  .008162{col 67}{space 1}   -2.24{col 76}{space 3}0.029{col 84}{space 4}-.0347209{col 97}{space 3}-.0019164
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1182157{col 56}{space 2} .0251242{col 67}{space 1}    4.71{col 76}{space 3}0.000{col 84}{space 4} .0677268{col 97}{space 3} .1687046
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2019}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     21,689    .1959058    .3969055          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -1.1942
{col 25}{txt}Prob>|t| = {res}    0.2783

95%{txt} confidence set for null hypothesis expression: {res}[−.1116, .02708] ∪ [.07359, .4727]
{err}2019

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3238 {c |}{res}      2,966       13.68       13.68
{txt}       3240 {c |}{res}      2,939       13.55       27.23
{txt}       3242 {c |}{res}     15,784       72.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     21,689      100.00

{txt}Linear regression                               Number of obs     = {res}     9,661
                                                {txt}F(18, 49)         =  {res}    42.42
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0290
                                                {txt}Root MSE          =    {res} .42381

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0135709{col 56}{space 2} .0149136{col 67}{space 1}   -0.91{col 76}{space 3}0.367{col 84}{space 4}-.0435409{col 97}{space 3} .0163991
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2}  .212199{col 56}{space 2} .0785336{col 67}{space 1}    2.70{col 76}{space 3}0.009{col 84}{space 4} .0543799{col 97}{space 3} .3700182
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0411634{col 56}{space 2}  .022684{col 67}{space 1}    1.81{col 76}{space 3}0.076{col 84}{space 4}-.0044219{col 97}{space 3} .0867486
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0684025{col 56}{space 2} .0249055{col 67}{space 1}    2.75{col 76}{space 3}0.008{col 84}{space 4} .0183529{col 97}{space 3}  .118452
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}  .071396{col 56}{space 2} .0251324{col 67}{space 1}    2.84{col 76}{space 3}0.007{col 84}{space 4} .0208905{col 97}{space 3} .1219014
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0913672{col 56}{space 2} .0254867{col 67}{space 1}    3.58{col 76}{space 3}0.001{col 84}{space 4} .0401497{col 97}{space 3} .1425847
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0942803{col 56}{space 2} .0258896{col 67}{space 1}    3.64{col 76}{space 3}0.001{col 84}{space 4} .0422532{col 97}{space 3} .1463075
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .1021708{col 56}{space 2} .0229974{col 67}{space 1}    4.44{col 76}{space 3}0.000{col 84}{space 4} .0559557{col 97}{space 3} .1483859
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0053024{col 56}{space 2} .0081056{col 67}{space 1}    0.65{col 76}{space 3}0.516{col 84}{space 4}-.0109863{col 97}{space 3} .0215911
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0014138{col 56}{space 2}  .000407{col 67}{space 1}    3.47{col 76}{space 3}0.001{col 84}{space 4} .0005958{col 97}{space 3} .0022318
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0565064{col 56}{space 2} .0131888{col 67}{space 1}   -4.28{col 76}{space 3}0.000{col 84}{space 4}-.0830103{col 97}{space 3}-.0300024
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} -.106453{col 56}{space 2}  .012147{col 67}{space 1}   -8.76{col 76}{space 3}0.000{col 84}{space 4}-.1308634{col 97}{space 3}-.0820426
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}  .027555{col 56}{space 2}  .017134{col 67}{space 1}    1.61{col 76}{space 3}0.114{col 84}{space 4} -.006877{col 97}{space 3} .0619869
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0483733{col 56}{space 2} .0153802{col 67}{space 1}    3.15{col 76}{space 3}0.003{col 84}{space 4} .0174657{col 97}{space 3} .0792809
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0168357{col 56}{space 2} .0171049{col 67}{space 1}    0.98{col 76}{space 3}0.330{col 84}{space 4}-.0175378{col 97}{space 3} .0512092
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0192978{col 56}{space 2} .0274188{col 67}{space 1}    0.70{col 76}{space 3}0.485{col 84}{space 4}-.0358023{col 97}{space 3} .0743979
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3273  {c |}{col 44}{res}{space 2} .0087079{col 56}{space 2} .0094605{col 67}{space 1}    0.92{col 76}{space 3}0.362{col 84}{space 4}-.0103036{col 97}{space 3} .0277194
{txt}{space 37}3277  {c |}{col 44}{res}{space 2} .0129123{col 56}{space 2} .0116293{col 67}{space 1}    1.11{col 76}{space 3}0.272{col 84}{space 4}-.0104577{col 97}{space 3} .0362823
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}  .067667{col 56}{space 2} .0375728{col 67}{space 1}    1.80{col 76}{space 3}0.078{col 84}{space 4}-.0078384{col 97}{space 3} .1431724
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2020}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      9,661    .2442811    .4296824          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.9100
{col 25}{txt}Prob>|t| = {res}    0.4194

95%{txt} confidence set for null hypothesis expression: {res}[−.08489, .09617]
{err}2020

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3271 {c |}{res}      2,893       29.95       29.95
{txt}       3273 {c |}{res}      2,915       30.17       60.12
{txt}       3277 {c |}{res}      3,853       39.88      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,661      100.00

{txt}Linear regression                               Number of obs     = {res}     9,913
                                                {txt}F(18, 49)         =  {res}     8.12
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0106
                                                {txt}Root MSE          =    {res} .40853

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2} .0002123{col 56}{space 2} .0083373{col 67}{space 1}    0.03{col 76}{space 3}0.980{col 84}{space 4}-.0165422{col 97}{space 3} .0169667
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0025231{col 56}{space 2} .0610158{col 67}{space 1}    0.04{col 76}{space 3}0.967{col 84}{space 4}-.1200927{col 97}{space 3}  .125139
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0082787{col 56}{space 2} .0207009{col 67}{space 1}   -0.40{col 76}{space 3}0.691{col 84}{space 4}-.0498787{col 97}{space 3} .0333212
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0070515{col 56}{space 2}  .025804{col 67}{space 1}   -0.27{col 76}{space 3}0.786{col 84}{space 4}-.0589066{col 97}{space 3} .0448036
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0063999{col 56}{space 2} .0251804{col 67}{space 1}   -0.25{col 76}{space 3}0.800{col 84}{space 4}-.0570018{col 97}{space 3}  .044202
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0069396{col 56}{space 2} .0229014{col 67}{space 1}   -0.30{col 76}{space 3}0.763{col 84}{space 4}-.0529617{col 97}{space 3} .0390825
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0032943{col 56}{space 2} .0232708{col 67}{space 1}   -0.14{col 76}{space 3}0.888{col 84}{space 4}-.0500588{col 97}{space 3} .0434701
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0262538{col 56}{space 2} .0242876{col 67}{space 1}    1.08{col 76}{space 3}0.285{col 84}{space 4}-.0225539{col 97}{space 3} .0750616
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0252834{col 56}{space 2} .0085374{col 67}{space 1}    2.96{col 76}{space 3}0.005{col 84}{space 4} .0081269{col 97}{space 3} .0424399
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0007385{col 56}{space 2} .0003769{col 67}{space 1}    1.96{col 76}{space 3}0.056{col 84}{space 4}-.0000189{col 97}{space 3} .0014959
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.007376{col 56}{space 2} .0116154{col 67}{space 1}   -0.64{col 76}{space 3}0.528{col 84}{space 4}-.0307179{col 97}{space 3} .0159659
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0368298{col 56}{space 2} .0114327{col 67}{space 1}   -3.22{col 76}{space 3}0.002{col 84}{space 4}-.0598047{col 97}{space 3}-.0138549
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0234213{col 56}{space 2} .0159982{col 67}{space 1}    1.46{col 76}{space 3}0.150{col 84}{space 4}-.0087283{col 97}{space 3} .0555709
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0466375{col 56}{space 2} .0131636{col 67}{space 1}    3.54{col 76}{space 3}0.001{col 84}{space 4} .0201843{col 97}{space 3} .0730907
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0145926{col 56}{space 2}  .027457{col 67}{space 1}    0.53{col 76}{space 3}0.597{col 84}{space 4}-.0405843{col 97}{space 3} .0697695
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0294683{col 56}{space 2} .0224024{col 67}{space 1}    1.32{col 76}{space 3}0.194{col 84}{space 4} -.015551{col 97}{space 3} .0744875
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3294  {c |}{col 44}{res}{space 2} .0092814{col 56}{space 2} .0085255{col 67}{space 1}    1.09{col 76}{space 3}0.282{col 84}{space 4}-.0078513{col 97}{space 3} .0264141
{txt}{space 37}3295  {c |}{col 44}{res}{space 2}  .006716{col 56}{space 2} .0079982{col 67}{space 1}    0.84{col 76}{space 3}0.405{col 84}{space 4}-.0093571{col 97}{space 3}  .022789
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1615889{col 56}{space 2}    .0425{col 67}{space 1}    3.80{col 76}{space 3}0.000{col 84}{space 4}  .076182{col 97}{space 3} .2469957
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2021}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      9,913    .2142641    .4103315          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}    0.0255
{col 25}{txt}Prob>|t| = {res}    0.9850

95%{txt} confidence set for null hypothesis expression: {res}[−.05546, .1759]
{err}2021

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3293 {c |}{res}      3,312       33.41       33.41
{txt}       3294 {c |}{res}      3,333       33.62       67.03
{txt}       3295 {c |}{res}      3,268       32.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,913      100.00

{txt}Linear regression                               Number of obs     = {res}    11,255
                                                {txt}F(18, 49)         =  {res}    44.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0215
                                                {txt}Root MSE          =    {res} .40483

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}top_prizes_gdp_ours {c |}{col 44}{res}{space 2}-.0005079{col 56}{space 2} .0203424{col 67}{space 1}   -0.02{col 76}{space 3}0.980{col 84}{space 4}-.0413876{col 97}{space 3} .0403717
{txt}{space 22}expenditure_gdp_ours {c |}{col 44}{res}{space 2} .0245052{col 56}{space 2} .0575749{col 67}{space 1}    0.43{col 76}{space 3}0.672{col 84}{space 4}-.0911959{col 97}{space 3} .1402062
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0386365{col 56}{space 2} .0189297{col 67}{space 1}   -2.04{col 76}{space 3}0.047{col 84}{space 4}-.0766772{col 97}{space 3}-.0005958
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0041615{col 56}{space 2} .0204794{col 67}{space 1}   -0.20{col 76}{space 3}0.840{col 84}{space 4}-.0453163{col 97}{space 3} .0369933
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0066195{col 56}{space 2} .0229262{col 67}{space 1}    0.29{col 76}{space 3}0.774{col 84}{space 4}-.0394525{col 97}{space 3} .0526914
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0037218{col 56}{space 2} .0194586{col 67}{space 1}   -0.19{col 76}{space 3}0.849{col 84}{space 4}-.0428253{col 97}{space 3} .0353818
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0093669{col 56}{space 2} .0249855{col 67}{space 1}    0.37{col 76}{space 3}0.709{col 84}{space 4}-.0408433{col 97}{space 3} .0595771
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0019318{col 56}{space 2} .0198315{col 67}{space 1}    0.10{col 76}{space 3}0.923{col 84}{space 4}-.0379211{col 97}{space 3} .0417847
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0459733{col 56}{space 2} .0074459{col 67}{space 1}    6.17{col 76}{space 3}0.000{col 84}{space 4} .0310103{col 97}{space 3} .0609364
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0014368{col 56}{space 2} .0005254{col 67}{space 1}    2.73{col 76}{space 3}0.009{col 84}{space 4} .0003809{col 97}{space 3} .0024926
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0428923{col 56}{space 2} .0117229{col 67}{space 1}   -3.66{col 76}{space 3}0.001{col 84}{space 4}-.0664504{col 97}{space 3}-.0193342
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0557975{col 56}{space 2} .0094618{col 67}{space 1}   -5.90{col 76}{space 3}0.000{col 84}{space 4}-.0748116{col 97}{space 3}-.0367834
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0138816{col 56}{space 2} .0124508{col 67}{space 1}    1.11{col 76}{space 3}0.270{col 84}{space 4}-.0111392{col 97}{space 3} .0389024
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0507747{col 56}{space 2} .0159406{col 67}{space 1}    3.19{col 76}{space 3}0.003{col 84}{space 4} .0187408{col 97}{space 3} .0828086
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}  .027258{col 56}{space 2} .0213912{col 67}{space 1}    1.27{col 76}{space 3}0.209{col 84}{space 4}-.0157291{col 97}{space 3} .0702452
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0147998{col 56}{space 2} .0205639{col 67}{space 1}   -0.72{col 76}{space 3}0.475{col 84}{space 4}-.0561245{col 97}{space 3} .0265248
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3305  {c |}{col 44}{res}{space 2} .0147444{col 56}{space 2} .0105648{col 67}{space 1}    1.40{col 76}{space 3}0.169{col 84}{space 4}-.0064863{col 97}{space 3} .0359751
{txt}{space 37}3306  {c |}{col 44}{res}{space 2}  .034919{col 56}{space 2} .0087056{col 67}{space 1}    4.01{col 76}{space 3}0.000{col 84}{space 4} .0174245{col 97}{space 3} .0524135
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1215859{col 56}{space 2}  .034482{col 67}{space 1}    3.53{col 76}{space 3}0.001{col 84}{space 4} .0522918{col 97}{space 3}   .19088
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2022}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     11,255    .2122612    .4089269          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_ours

{txt}{col 28}t(49) = {res}   -0.0250
{col 25}{txt}Prob>|t| = {res}    0.9860

95%{txt} confidence set for null hypothesis expression: {res}[−.08668, .1155]
{err}2022

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3304 {c |}{res}      3,680       32.70       32.70
{txt}       3305 {c |}{res}      3,755       33.36       66.06
{txt}       3306 {c |}{res}      3,820       33.94      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     11,255      100.00
{txt}
{com}. 
. * Step 2: Save the Results to a New Dataset
. 
. * Double-check the matrix contents before conversion
. matrix list bootstrap_results
{res}
{txt}bootstrap_results[36,3]
             c1          c2          c3
 r1 {res}  .03752698  -1.0422311   .35957491
{txt} r2 {res}  .03753736  -1.0317265   .22324814
{txt} r3 {res} -.11059541  -.87501163   .65726021
{txt} r4 {res}  .04235635  -.20983174   .26003148
{txt} r5 {res} -.06834551  -.46126176   .55986724
{txt} r6 {res}  .01477791  -.13348491   .36522085
{txt} r7 {res}  .00035655  -.29066875   .69051979
{txt} r8 {res}   .0020376  -.30987339   .16190328
{txt} r9 {res} -.01218082  -.34205291   .09208962
{txt}r10 {res} -.02015724  -.63704812   .44079152
{txt}r11 {res}    .171277  -.05838701   .58448978
{txt}r12 {res} -.02094283  -.46871546   .44485923
{txt}r13 {res} -.10670482  -.45498711   .16731689
{txt}r14 {res}  .11263495  -.14365478   .86388538
{txt}r15 {res} -.00441935  -.10683754   .14691258
{txt}r16 {res}  .09669304  -.09988969   .35416731
{txt}r17 {res}   .0146906  -.07722049   .13575285
{txt}r18 {res} -.06415804  -.23856687   .26981971
{txt}r19 {res} -.01206217  -.10967849   .20520008
{txt}r20 {res}  .14347402  -.68593012    1.292215
{txt}r21 {res}  .10535452  -.50839495   .55543746
{txt}r22 {res}  -.0072746  -.45112969   .35742747
{txt}r23 {res}  .10598609  -.26693833   .45271441
{txt}r24 {res} -.12344227  -.55035653   .30641271
{txt}r25 {res}  .20231161  -.04128278   .66953665
{txt}r26 {res}  .04519538  -2.1191669   .75640123
{txt}r27 {res} -.00888719  -.54844224   .35788138
{txt}r28 {res}  .03326161   -.1043939   .72226959
{txt}r29 {res}  .01100074  -.16678958   .09858876
{txt}r30 {res}  .04007786  -.20940855   .38599319
{txt}r31 {res}  .21616703  -.47848105   .62346508
{txt}r32 {res} -.00535869  -.09366094   .17519379
{txt}r33 {res} -.01529063  -.11160374   .02707564
{txt}r34 {res} -.01357091   -.0848857   .09617008
{txt}r35 {res}  .00021227  -.05546241   .17585372
{txt}r36 {res} -.00050793  -.08667822   .11545829
{reset}
{com}. 
. * Save the results to a new dataset
. svmat double bootstrap_results, names(col)
{txt}
{com}. rename c1 top_prizes_coefficient
{res}{txt}
{com}. rename c2 top_prizes_ci_lower
{res}{txt}
{com}. rename c3 top_prizes_ci_upper
{res}{txt}
{com}. 
. * Step 3: Clean the Dataset and Calculate Standard Errors
. 
. * Drop rows where the coefficients or confidence intervals are missing
. drop if missing(top_prizes_coefficient) & missing(top_prizes_ci_lower) & missing(top_prizes_ci_upper) 
{txt}(346,282 observations deleted)

{com}. 
. * Keep only the relevant variables for the meta-analysis
. keep top_prizes_coefficient top_prizes_ci_lower top_prizes_ci_upper
{txt}
{com}. 
. * Generate a new year variable for the meta-analysis
. gen year = 1987 + _n - 1  // Adjust the starting year if necessary
{txt}
{com}. 
. * Calculate standard errors from the confidence intervals
. gen top_prizes_se = (top_prizes_ci_upper - top_prizes_ci_lower) / (2 * 1.96)
{txt}
{com}. 
. * Step 4: Meta-Analysis for top_prizes_gdp and expenditure_gdp
. 
. * Ensure that the year variable is correctly set and does not have duplicate values
. sort year
{txt}
{com}. 
. *** Pooled:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}36
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}    36
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             1987{col 19}{c |}{res}{space 6}    0.038{col 35}{space 3}   -0.663{col 47}{space 3}    0.738{col 59}{space 5} 0.20
{col 1}{txt}             1988{col 19}{c |}{res}{space 6}    0.038{col 35}{space 3}   -0.590{col 47}{space 3}    0.665{col 59}{space 5} 0.25
{col 1}{txt}             1989{col 19}{c |}{res}{space 6}   -0.111{col 35}{space 3}   -0.877{col 47}{space 3}    0.656{col 59}{space 5} 0.17
{col 1}{txt}             1990{col 19}{c |}{res}{space 6}    0.042{col 35}{space 3}   -0.193{col 47}{space 3}    0.277{col 59}{space 5} 1.78
{col 1}{txt}             1991{col 19}{c |}{res}{space 6}   -0.068{col 35}{space 3}   -0.579{col 47}{space 3}    0.442{col 59}{space 5} 0.38
{col 1}{txt}             1992{col 19}{c |}{res}{space 6}    0.015{col 35}{space 3}   -0.235{col 47}{space 3}    0.264{col 59}{space 5} 1.58
{col 1}{txt}             1993{col 19}{c |}{res}{space 6}    0.000{col 35}{space 3}   -0.490{col 47}{space 3}    0.491{col 59}{space 5} 0.41
{col 1}{txt}             1994{col 19}{c |}{res}{space 6}    0.002{col 35}{space 3}   -0.234{col 47}{space 3}    0.238{col 59}{space 5} 1.77
{col 1}{txt}             1995{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.229{col 47}{space 3}    0.205{col 59}{space 5} 2.09
{col 1}{txt}             1996{col 19}{c |}{res}{space 6}   -0.020{col 35}{space 3}   -0.559{col 47}{space 3}    0.519{col 59}{space 5} 0.34
{col 1}{txt}             1997{col 19}{c |}{res}{space 6}    0.171{col 35}{space 3}   -0.150{col 47}{space 3}    0.493{col 59}{space 5} 0.95
{col 1}{txt}             1998{col 19}{c |}{res}{space 6}   -0.021{col 35}{space 3}   -0.478{col 47}{space 3}    0.436{col 59}{space 5} 0.47
{col 1}{txt}             1999{col 19}{c |}{res}{space 6}   -0.107{col 35}{space 3}   -0.418{col 47}{space 3}    0.204{col 59}{space 5} 1.01
{col 1}{txt}             2000{col 19}{c |}{res}{space 6}    0.113{col 35}{space 3}   -0.391{col 47}{space 3}    0.616{col 59}{space 5} 0.39
{col 1}{txt}             2001{col 19}{c |}{res}{space 6}   -0.004{col 35}{space 3}   -0.131{col 47}{space 3}    0.122{col 59}{space 5} 6.10
{col 1}{txt}             2002{col 19}{c |}{res}{space 6}    0.097{col 35}{space 3}   -0.130{col 47}{space 3}    0.324{col 59}{space 5} 1.91
{col 1}{txt}             2003{col 19}{c |}{res}{space 6}    0.015{col 35}{space 3}   -0.092{col 47}{space 3}    0.121{col 59}{space 5} 8.67
{col 1}{txt}             2004{col 19}{c |}{res}{space 6}   -0.064{col 35}{space 3}   -0.318{col 47}{space 3}    0.190{col 59}{space 5} 1.52
{col 1}{txt}             2005{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.169{col 47}{space 3}    0.145{col 59}{space 5} 3.96
{col 1}{txt}             2006{col 19}{c |}{res}{space 6}    0.143{col 35}{space 3}   -0.846{col 47}{space 3}    1.133{col 59}{space 5} 0.10
{col 1}{txt}             2007{col 19}{c |}{res}{space 6}    0.105{col 35}{space 3}   -0.427{col 47}{space 3}    0.637{col 59}{space 5} 0.35
{col 1}{txt}             2008{col 19}{c |}{res}{space 6}   -0.007{col 35}{space 3}   -0.412{col 47}{space 3}    0.397{col 59}{space 5} 0.60
{col 1}{txt}             2009{col 19}{c |}{res}{space 6}    0.106{col 35}{space 3}   -0.254{col 47}{space 3}    0.466{col 59}{space 5} 0.76
{col 1}{txt}             2010{col 19}{c |}{res}{space 6}   -0.123{col 35}{space 3}   -0.552{col 47}{space 3}    0.305{col 59}{space 5} 0.54
{col 1}{txt}             2011{col 19}{c |}{res}{space 6}    0.202{col 35}{space 3}   -0.153{col 47}{space 3}    0.558{col 59}{space 5} 0.78
{col 1}{txt}             2012{col 19}{c |}{res}{space 6}    0.045{col 35}{space 3}   -1.393{col 47}{space 3}    1.483{col 59}{space 5} 0.05
{col 1}{txt}             2013{col 19}{c |}{res}{space 6}   -0.009{col 35}{space 3}   -0.462{col 47}{space 3}    0.444{col 59}{space 5} 0.48
{col 1}{txt}             2014{col 19}{c |}{res}{space 6}    0.033{col 35}{space 3}   -0.380{col 47}{space 3}    0.447{col 59}{space 5} 0.58
{col 1}{txt}             2015{col 19}{c |}{res}{space 6}    0.011{col 35}{space 3}   -0.122{col 47}{space 3}    0.144{col 59}{space 5} 5.58
{col 1}{txt}             2016{col 19}{c |}{res}{space 6}    0.040{col 35}{space 3}   -0.258{col 47}{space 3}    0.338{col 59}{space 5} 1.11
{col 1}{txt}             2017{col 19}{c |}{res}{space 6}    0.216{col 35}{space 3}   -0.335{col 47}{space 3}    0.767{col 59}{space 5} 0.32
{col 1}{txt}             2018{col 19}{c |}{res}{space 6}   -0.005{col 35}{space 3}   -0.140{col 47}{space 3}    0.129{col 59}{space 5} 5.44
{col 1}{txt}             2019{col 19}{c |}{res}{space 6}   -0.015{col 35}{space 3}   -0.085{col 47}{space 3}    0.054{col 59}{space 5}20.44
{col 1}{txt}             2020{col 19}{c |}{res}{space 6}   -0.014{col 35}{space 3}   -0.104{col 47}{space 3}    0.077{col 59}{space 5}11.99
{col 1}{txt}             2021{col 19}{c |}{res}{space 6}    0.000{col 35}{space 3}   -0.115{col 47}{space 3}    0.116{col 59}{space 5} 7.35
{col 1}{txt}             2022{col 19}{c |}{res}{space 6}   -0.001{col 35}{space 3}   -0.102{col 47}{space 3}    0.101{col 59}{space 5} 9.62
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.002{col 35}{space 3}   -0.030{col 47}{space 3}    0.033
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.11{txt}{col 50}Prob > |z| = {res}0.9134
{txt}Test of homogeneity: Q = chi2({res}35{txt}) = {res}6.23{txt}{col 52}Prob > Q = {res}1.0000
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_top_prizes_pooled.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_top_prizes_pooled.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. preserve 
{txt}
{com}. 
. keep if year<2010
{txt}(13 observations deleted)

{com}. 
. *** Pre:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}23
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}    23
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             1987{col 19}{c |}{res}{space 6}    0.038{col 35}{space 3}   -0.663{col 47}{space 3}    0.738{col 59}{space 5} 0.56
{col 1}{txt}             1988{col 19}{c |}{res}{space 6}    0.038{col 35}{space 3}   -0.590{col 47}{space 3}    0.665{col 59}{space 5} 0.70
{col 1}{txt}             1989{col 19}{c |}{res}{space 6}   -0.111{col 35}{space 3}   -0.877{col 47}{space 3}    0.656{col 59}{space 5} 0.47
{col 1}{txt}             1990{col 19}{c |}{res}{space 6}    0.042{col 35}{space 3}   -0.193{col 47}{space 3}    0.277{col 59}{space 5} 4.98
{col 1}{txt}             1991{col 19}{c |}{res}{space 6}   -0.068{col 35}{space 3}   -0.579{col 47}{space 3}    0.442{col 59}{space 5} 1.05
{col 1}{txt}             1992{col 19}{c |}{res}{space 6}    0.015{col 35}{space 3}   -0.235{col 47}{space 3}    0.264{col 59}{space 5} 4.42
{col 1}{txt}             1993{col 19}{c |}{res}{space 6}    0.000{col 35}{space 3}   -0.490{col 47}{space 3}    0.491{col 59}{space 5} 1.14
{col 1}{txt}             1994{col 19}{c |}{res}{space 6}    0.002{col 35}{space 3}   -0.234{col 47}{space 3}    0.238{col 59}{space 5} 4.94
{col 1}{txt}             1995{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.229{col 47}{space 3}    0.205{col 59}{space 5} 5.83
{col 1}{txt}             1996{col 19}{c |}{res}{space 6}   -0.020{col 35}{space 3}   -0.559{col 47}{space 3}    0.519{col 59}{space 5} 0.95
{col 1}{txt}             1997{col 19}{c |}{res}{space 6}    0.171{col 35}{space 3}   -0.150{col 47}{space 3}    0.493{col 59}{space 5} 2.66
{col 1}{txt}             1998{col 19}{c |}{res}{space 6}   -0.021{col 35}{space 3}   -0.478{col 47}{space 3}    0.436{col 59}{space 5} 1.32
{col 1}{txt}             1999{col 19}{c |}{res}{space 6}   -0.107{col 35}{space 3}   -0.418{col 47}{space 3}    0.204{col 59}{space 5} 2.84
{col 1}{txt}             2000{col 19}{c |}{res}{space 6}    0.113{col 35}{space 3}   -0.391{col 47}{space 3}    0.616{col 59}{space 5} 1.08
{col 1}{txt}             2001{col 19}{c |}{res}{space 6}   -0.004{col 35}{space 3}   -0.131{col 47}{space 3}    0.122{col 59}{space 5}17.08
{col 1}{txt}             2002{col 19}{c |}{res}{space 6}    0.097{col 35}{space 3}   -0.130{col 47}{space 3}    0.324{col 59}{space 5} 5.33
{col 1}{txt}             2003{col 19}{c |}{res}{space 6}    0.015{col 35}{space 3}   -0.092{col 47}{space 3}    0.121{col 59}{space 5}24.24
{col 1}{txt}             2004{col 19}{c |}{res}{space 6}   -0.064{col 35}{space 3}   -0.318{col 47}{space 3}    0.190{col 59}{space 5} 4.25
{col 1}{txt}             2005{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.169{col 47}{space 3}    0.145{col 59}{space 5}11.09
{col 1}{txt}             2006{col 19}{c |}{res}{space 6}    0.143{col 35}{space 3}   -0.846{col 47}{space 3}    1.133{col 59}{space 5} 0.28
{col 1}{txt}             2007{col 19}{c |}{res}{space 6}    0.105{col 35}{space 3}   -0.427{col 47}{space 3}    0.637{col 59}{space 5} 0.97
{col 1}{txt}             2008{col 19}{c |}{res}{space 6}   -0.007{col 35}{space 3}   -0.412{col 47}{space 3}    0.397{col 59}{space 5} 1.68
{col 1}{txt}             2009{col 19}{c |}{res}{space 6}    0.106{col 35}{space 3}   -0.254{col 47}{space 3}    0.466{col 59}{space 5} 2.12
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.011{col 35}{space 3}   -0.041{col 47}{space 3}    0.064
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.42{txt}{col 50}Prob > |z| = {res}0.6776
{txt}Test of homogeneity: Q = chi2({res}22{txt}) = {res}3.51{txt}{col 52}Prob > Q = {res}1.0000
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_top_prizes_pre.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_top_prizes_pre.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. restore
{txt}
{com}. 
. preserve 
{txt}
{com}. 
. keep if year>=2010
{txt}(23 observations deleted)

{com}. 
. **** Post:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}13
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}    13
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             2010{col 19}{c |}{res}{space 6}   -0.123{col 35}{space 3}   -0.552{col 47}{space 3}    0.305{col 59}{space 5} 0.83
{col 1}{txt}             2011{col 19}{c |}{res}{space 6}    0.202{col 35}{space 3}   -0.153{col 47}{space 3}    0.558{col 59}{space 5} 1.21
{col 1}{txt}             2012{col 19}{c |}{res}{space 6}    0.045{col 35}{space 3}   -1.393{col 47}{space 3}    1.483{col 59}{space 5} 0.07
{col 1}{txt}             2013{col 19}{c |}{res}{space 6}   -0.009{col 35}{space 3}   -0.462{col 47}{space 3}    0.444{col 59}{space 5} 0.74
{col 1}{txt}             2014{col 19}{c |}{res}{space 6}    0.033{col 35}{space 3}   -0.380{col 47}{space 3}    0.447{col 59}{space 5} 0.90
{col 1}{txt}             2015{col 19}{c |}{res}{space 6}    0.011{col 35}{space 3}   -0.122{col 47}{space 3}    0.144{col 59}{space 5} 8.69
{col 1}{txt}             2016{col 19}{c |}{res}{space 6}    0.040{col 35}{space 3}   -0.258{col 47}{space 3}    0.338{col 59}{space 5} 1.73
{col 1}{txt}             2017{col 19}{c |}{res}{space 6}    0.216{col 35}{space 3}   -0.335{col 47}{space 3}    0.767{col 59}{space 5} 0.50
{col 1}{txt}             2018{col 19}{c |}{res}{space 6}   -0.005{col 35}{space 3}   -0.140{col 47}{space 3}    0.129{col 59}{space 5} 8.46
{col 1}{txt}             2019{col 19}{c |}{res}{space 6}   -0.015{col 35}{space 3}   -0.085{col 47}{space 3}    0.054{col 59}{space 5}31.80
{col 1}{txt}             2020{col 19}{c |}{res}{space 6}   -0.014{col 35}{space 3}   -0.104{col 47}{space 3}    0.077{col 59}{space 5}18.66
{col 1}{txt}             2021{col 19}{c |}{res}{space 6}    0.000{col 35}{space 3}   -0.115{col 47}{space 3}    0.116{col 59}{space 5}11.43
{col 1}{txt}             2022{col 19}{c |}{res}{space 6}   -0.001{col 35}{space 3}   -0.102{col 47}{space 3}    0.101{col 59}{space 5}14.97
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}   -0.003{col 35}{space 3}   -0.043{col 47}{space 3}    0.036
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}-0.17{txt}{col 50}Prob > |z| = {res}0.8616
{txt}Test of homogeneity: Q = chi2({res}12{txt}) = {res}2.53{txt}{col 52}Prob > Q = {res}0.9980
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_top_prizes_post.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_top_prizes_post.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. * Combine meta-analysis results into a single model -- place holder for meta analyses 
. eststo meta_combined: estadd scalar top_prizes_gdp_ours = top_prizes_meta_coef

{txt}added scalar:
e(top_prizes_gdp_ours) =  {res}-.00347914
{txt}
{com}. estadd scalar top_prizes_se = top_prizes_meta_se

{txt}added scalar:
      e(top_prizes_se) =  {res}.01995148
{txt}
{com}. 
. restore
{txt}
{com}. 
. * Step 5: Generate LaTeX Tables. Appendix Table 27
. 
. *  Appendix Table 27. Part 1: years 1987 to 1996
. esttab boots_Q1_1987 boots_Q1_1988 boots_Q1_1989 boots_Q1_1990 boots_Q1_1991 boots_Q1_1992 boots_Q1_1993 boots_Q1_1994 boots_Q1_1995 boots_Q1_1996  ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table_27_Part1_1987_1996.tex, ///
>     keep(top_prizes_gdp_ours) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_ours "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_27_Part1_1987_1996.tex"'})

{com}.         
. 
. * Appendix Table 27. Part 2: years 1997 to 2006
. esttab boots_Q1_1997 boots_Q1_1998 boots_Q1_1999 boots_Q1_2000 boots_Q1_2001 boots_Q1_2002 boots_Q1_2003 boots_Q1_2004 boots_Q1_2005 boots_Q1_2006  ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table_27_Part2_1997_2006.tex, ///
>     keep(top_prizes_gdp_ours) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_ours "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_27_Part2_1997_2006.tex"'})

{com}. 
. 
. * Appendix Table 27. Part 3: years 2007 to 2016
. esttab boots_Q1_2007 boots_Q1_2008 boots_Q1_2009 boots_Q1_2010 boots_Q1_2011 boots_Q1_2012 boots_Q1_2013 boots_Q1_2014 boots_Q1_2015 boots_Q1_2016  ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table_27_Part3_2007_2016.tex, ///
>     keep(top_prizes_gdp_ours) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_ours "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_27_Part3_2007_2016.tex"'})

{com}. 
. 
. * Appendix Table 27. Part 4: years 2017 to 2022 + meta analyses
. esttab boots_Q1_2017 boots_Q1_2018 boots_Q1_2019 boots_Q1_2020 boots_Q1_2021 boots_Q1_2022 meta_combined meta_combined meta_combined ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table_27_Part4_2018_2022_meta.tex, ///
>     keep(top_prizes_gdp_ours) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_ours "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_27_Part4_2018_2022_meta.tex"'})

{com}.         
. 
. 
. /* NOTE: Information of the last 3 columns of the table (the meta-analysis in cols 37-39) and 
> WCB confidence sets are manually added to the tables from the Stata output generated here.
> */
. 
. 
. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLE 28: Survey Results Using Lottery Prizes Cumulated over 
. *** Electoral Term, by Year and Estimation Type, Using Our Data. Outcomes 
. *** measured only in the month before the election
. **----------------------------------------------------------------------------**
. 
. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. ** Month (for which we have survery data) before the election: 
. gen preelectoral = 1 if yearmonth==tm(1986m5) | yearmonth==tm(1989m9) | ///
> yearmonth==tm(1993m5) | yearmonth==tm(1996m2) | yearmonth==tm(2000m2) | ///
> yearmonth==tm(2004m1) | yearmonth==tm(2008m1) | yearmonth==tm(2011m7) | ///
> yearmonth==tm(2015m10) | yearmonth==tm(2016m4)| yearmonth==tm(2019m3) 
{txt}(1,023,640 missing values generated)

{com}. ** Merge based on yearmonth province. 
. 
. preserve
{txt}
{com}. 
. ***
. 
. * read electoral data
. use "Data/Our_data/20250531_SpanishLottery_Complete_province.dta", clear 
{txt}
{com}. 
. replace yearmonth = yearmonth
{txt}(0 real changes made)

{com}. format yearmonth %tm
{txt}
{com}. label var yearmonth "Year-Month"
{txt}
{com}. 
. /*This is to match month of election in electoral data to pre month in survey 
> data. In any case, what I'm merging is the cumulative prize data */
. replace yearmonth = tm(1986m5) if yearmonth == tm(1986m6)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(1989m9) if yearmonth == tm(1989m10)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(1993m5) if yearmonth == tm(1993m6)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(1996m2) if yearmonth == tm(1996m3)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2000m2) if yearmonth == tm(2000m3)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2004m1) if yearmonth == tm(2004m3)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2008m1) if yearmonth == tm(2008m3)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2011m7) if yearmonth == tm(2011m11)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2015m10) if yearmonth == tm(2015m12)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2016m4) if yearmonth == tm(2016m6)
{txt}(50 real changes made)

{com}. replace yearmonth = tm(2019m3) if yearmonth == tm(2019m4)
{txt}(50 real changes made)

{com}. 
. 
. keep top_prizes_gdp_term_2 expenditure_gdp_term_2 year month yearmonth province_num province 
{txt}
{com}. 
. 
. ** province var is already clean: lower cased.
. 
. tempfile electoral_mon
{txt}
{com}. 
. save `electoral_mon'
{txt}{p 0 4 2}
file {bf}
/var/folders/54/7cxl8ny95nb4vs26j1q2yq9r0000gn/T//S_08037.00000f{rm}
saved
as .dta format
{p_end}

{com}. 
. restore /*Load survey data back*/
{txt}
{com}. 
. ***
. 
. ** Keep only observations that correspond to the (available) month before election.
. keep if preelectoral==1
{txt}(1,023,640 observations deleted)

{com}. 
. merge m:1 yearmonth province using `electoral_mon'
{res}{txt}{p 0 7 2}
(variable
{bf:month} was {bf:byte}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}             165
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}             165{txt}  (_merge==2)

{col 5}Matched{col 30}{res}         105,870{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge!=3 /*1986, 2019-11, 2023 didn't merge b/c no survey data for them */
{txt}(165 observations deleted)

{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. global individual_characteristics "i.municipality_size female age i.education i.status"
{txt}
{com}. 
. ** Row number of matrix
. gen row = 1 if year_original == 1989
{txt}(100,893 missing values generated)

{com}. replace row = 2 if year_original == 1990
{txt}(0 real changes made)

{com}. replace row = 3 if year_original == 1993
{txt}(2,496 real changes made)

{com}. replace row = 4 if year_original == 1996
{txt}(2,491 real changes made)

{com}. replace row = 5 if year_original == 2000 
{txt}(26,039 real changes made)

{com}. replace row = 6 if year_original == 2004
{txt}(26,120 real changes made)

{com}. replace row = 7 if year_original == 2008
{txt}(20,307 real changes made)

{com}. replace row = 8 if year_original == 2011
{txt}(2,475 real changes made)

{com}. replace row = 9 if year_original == 2015
{txt}(2,485 real changes made)

{com}. replace row = 10 if year_original == 2019
{txt}(15,998 real changes made)

{com}. 
. * Step 1: Initialize and Loop through All Years
. 
. local regression_names ""
{txt}
{com}. 
. * Initialize the matrix with 10 rows (electoral years from 1989 to 2019) and 6 columns (coefficients, lower bounds, upper bounds)
. matrix bootstrap_results = J(31, 3, .) 
{txt}
{com}. 
. foreach y in 1989 1993 1996 2000 2004 2008 2011 2015 2016 2019 {c -(}
{txt}  2{com}.     * Run the regression for the current year
.     eststo boots_pre_m_`y': reg vote_incumbent top_prizes_gdp_term_2 expenditure_gdp_term_2 $individual_characteristics i.survey if year_original ==`y', cluster(prov_num)
{txt}  3{com}.         
. 
.         * Labels:
.         estadd local SurveyFE "$\checkmark$"
{txt}  4{com}.         estadd local Estimation "OLS"
{txt}  5{com}.         estadd local Data "Ours"
{txt}  6{com}.         estadd local Outcome "pre_m"
{txt}  7{com}.         estadd local ProvinceFE "$\times$"
{txt}  8{com}.         estadd local Year "`y'"
{txt}  9{com}.         
.         sum vote_incumbent if e(sample)==1
{txt} 10{com}.         
.     * Run boottest for top_prizes_gdp
.         boottest top_prizes_gdp_term_2, cluster(prov_num) seed(12345)
{txt} 11{com}.         
.     * Capture confidence sets from the r(CI) matrix (these are WCB):
.         matrix top_prizes_CI = r(CI)
{txt} 12{com}.     local top_prizes_ci_lower = top_prizes_CI[1,1]
{txt} 13{com}.     local top_prizes_ci_upper = top_prizes_CI[1,2]
{txt} 14{com}.                     
.     
.     * Calculate the row number based on the year
.     local row = `y' - 1988  // 1989 starts at row 1, 1993 at row 5, etc.
{txt} 15{com}.     
.     * Store the coefficients and both confidence interval bounds in the matrix
.     matrix bootstrap_results[`row', 1] = _b[top_prizes_gdp_term_2]
{txt} 16{com}.         matrix bootstrap_results[`row', 2] = `top_prizes_ci_lower'
{txt} 17{com}.     matrix bootstrap_results[`row', 3] = `top_prizes_ci_upper'
{txt} 18{com}. 
.             
.     * Add regression name to the list
.     local regression_names "`regression_names' boots_pre_m_`y'"
{txt} 19{com}.         
.         dis as error `y'
{txt} 20{com}.         
.         tab survey if e(sample)==1
{txt} 21{com}.         
. {c )-}

{txt}Linear regression                               Number of obs     = {res}     4,843
                                                {txt}F(17, 48)         =  {res}    17.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0360
                                                {txt}Root MSE          =    {res} .45415

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0055643{col 56}{space 2} .0220177{col 67}{space 1}   -0.25{col 76}{space 3}0.802{col 84}{space 4}-.0498339{col 97}{space 3} .0387054
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.1325837{col 56}{space 2} .0718766{col 67}{space 1}   -1.84{col 76}{space 3}0.071{col 84}{space 4}-.2771013{col 97}{space 3} .0119338
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}  .052615{col 56}{space 2} .0344264{col 67}{space 1}    1.53{col 76}{space 3}0.133{col 84}{space 4} -.016604{col 97}{space 3}  .121834
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0740079{col 56}{space 2} .0366564{col 67}{space 1}    2.02{col 76}{space 3}0.049{col 84}{space 4} .0003052{col 97}{space 3} .1477106
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}  .112288{col 56}{space 2} .0414495{col 67}{space 1}    2.71{col 76}{space 3}0.009{col 84}{space 4} .0289482{col 97}{space 3} .1956277
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0656021{col 56}{space 2}  .036692{col 67}{space 1}    1.79{col 76}{space 3}0.080{col 84}{space 4} -.008172{col 97}{space 3} .1393763
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0042372{col 56}{space 2} .0414288{col 67}{space 1}   -0.10{col 76}{space 3}0.919{col 84}{space 4}-.0875354{col 97}{space 3}  .079061
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0498292{col 56}{space 2} .0329694{col 67}{space 1}    1.51{col 76}{space 3}0.137{col 84}{space 4}-.0164603{col 97}{space 3} .1161186
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0024081{col 56}{space 2} .0178532{col 67}{space 1}   -0.13{col 76}{space 3}0.893{col 84}{space 4}-.0383043{col 97}{space 3} .0334881
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0025291{col 56}{space 2} .0006248{col 67}{space 1}   -4.05{col 76}{space 3}0.000{col 84}{space 4}-.0037853{col 97}{space 3}-.0012729
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.1206638{col 56}{space 2} .0181937{col 67}{space 1}   -6.63{col 76}{space 3}0.000{col 84}{space 4}-.1572446{col 97}{space 3} -.084083
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1849654{col 56}{space 2} .0231055{col 67}{space 1}   -8.01{col 76}{space 3}0.000{col 84}{space 4}-.2314221{col 97}{space 3}-.1385088
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0204625{col 56}{space 2} .0295523{col 67}{space 1}    0.69{col 76}{space 3}0.492{col 84}{space 4}-.0389563{col 97}{space 3} .0798813
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .1089861{col 56}{space 2} .0235167{col 67}{space 1}    4.63{col 76}{space 3}0.000{col 84}{space 4} .0617025{col 97}{space 3} .1562697
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0647066{col 56}{space 2} .0334877{col 67}{space 1}   -1.93{col 76}{space 3}0.059{col 84}{space 4} -.132038{col 97}{space 3} .0026249
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0045382{col 56}{space 2}  .025949{col 67}{space 1}    0.17{col 76}{space 3}0.862{col 84}{space 4}-.0476357{col 97}{space 3} .0567121
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1840  {c |}{col 44}{res}{space 2} .0114093{col 56}{space 2} .0170533{col 67}{space 1}    0.67{col 76}{space 3}0.507{col 84}{space 4}-.0228787{col 97}{space 3} .0456973
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .4902411{col 56}{space 2} .0782088{col 67}{space 1}    6.27{col 76}{space 3}0.000{col 84}{space 4} .3329919{col 97}{space 3} .6474904
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1989}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      4,843    .3080735    .4617448          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(48) = {res}   -0.2527
{col 25}{txt}Prob>|t| = {res}    0.8699

95%{txt} confidence set for null hypothesis expression: {res}[−.1697, .1647]
{err}1989

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1839 {c |}{res}      2,424       50.05       50.05
{txt}       1840 {c |}{res}      2,419       49.95      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,843      100.00
{txt}{p 0 6 2}note: {bf:2059.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,459
                                                {txt}F(16, 41)         =  {res}     5.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0353
                                                {txt}Root MSE          =    {res} .42235

{txt}{ralign 108:(Std. err. adjusted for {res:42} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0629764{col 56}{space 2} .0335596{col 67}{space 1}   -1.88{col 76}{space 3}0.068{col 84}{space 4}-.1307514{col 97}{space 3} .0047985
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0043292{col 56}{space 2} .0495392{col 67}{space 1}   -0.09{col 76}{space 3}0.931{col 84}{space 4}-.1043757{col 97}{space 3} .0957173
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0103543{col 56}{space 2} .0369151{col 67}{space 1}   -0.28{col 76}{space 3}0.781{col 84}{space 4}-.0849058{col 97}{space 3} .0641973
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0137815{col 56}{space 2} .0408122{col 67}{space 1}    0.34{col 76}{space 3}0.737{col 84}{space 4}-.0686404{col 97}{space 3} .0962034
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0061281{col 56}{space 2}    .0478{col 67}{space 1}   -0.13{col 76}{space 3}0.899{col 84}{space 4}-.1026622{col 97}{space 3}  .090406
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0354359{col 56}{space 2} .0414108{col 67}{space 1}   -0.86{col 76}{space 3}0.397{col 84}{space 4}-.1190668{col 97}{space 3}  .048195
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0873536{col 56}{space 2} .0800484{col 67}{space 1}    1.09{col 76}{space 3}0.282{col 84}{space 4}-.0743074{col 97}{space 3} .2490146
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0191592{col 56}{space 2} .0460384{col 67}{space 1}    0.42{col 76}{space 3}0.679{col 84}{space 4}-.0738173{col 97}{space 3} .1121357
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0207032{col 56}{space 2} .0239699{col 67}{space 1}    0.86{col 76}{space 3}0.393{col 84}{space 4}-.0277049{col 97}{space 3} .0691113
{txt}{space 39}age {c |}{col 44}{res}{space 2}   .00051{col 56}{space 2} .0008949{col 67}{space 1}    0.57{col 76}{space 3}0.572{col 84}{space 4}-.0012973{col 97}{space 3} .0023173
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0666448{col 56}{space 2} .0235631{col 67}{space 1}   -2.83{col 76}{space 3}0.007{col 84}{space 4}-.1142314{col 97}{space 3}-.0190582
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} -.148023{col 56}{space 2} .0328195{col 67}{space 1}   -4.51{col 76}{space 3}0.000{col 84}{space 4}-.2143033{col 97}{space 3}-.0817426
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}  .018109{col 56}{space 2} .0267866{col 67}{space 1}    0.68{col 76}{space 3}0.503{col 84}{space 4}-.0359876{col 97}{space 3} .0722056
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0909279{col 56}{space 2} .0394893{col 67}{space 1}    2.30{col 76}{space 3}0.026{col 84}{space 4} .0111777{col 97}{space 3}  .170678
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} -.030158{col 56}{space 2}  .025042{col 67}{space 1}   -1.20{col 76}{space 3}0.235{col 84}{space 4}-.0807313{col 97}{space 3} .0204154
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0183428{col 56}{space 2} .0367625{col 67}{space 1}   -0.50{col 76}{space 3}0.620{col 84}{space 4}-.0925862{col 97}{space 3} .0559005
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2059  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2502029{col 56}{space 2} .0727413{col 67}{space 1}    3.44{col 76}{space 3}0.001{col 84}{space 4} .1032988{col 97}{space 3}  .397107
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1993}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,459    .2423749    .4286071          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(41) = {res}   -1.8766
{col 25}{txt}Prob>|t| = {res}    0.0581

95%{txt} confidence set for null hypothesis expression: {res}[−.2061, .002745]
{err}1993

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2059 {c |}{res}      2,459      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,459      100.00
{txt}{p 0 6 2}note: {bf:2208.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,464
                                                {txt}F(16, 47)         =  {res}     5.13
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0212
                                                {txt}Root MSE          =    {res} .43901

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0091901{col 56}{space 2} .0445424{col 67}{space 1}    0.21{col 76}{space 3}0.837{col 84}{space 4}-.0804176{col 97}{space 3} .0987977
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0292926{col 56}{space 2}  .078882{col 67}{space 1}   -0.37{col 76}{space 3}0.712{col 84}{space 4}-.1879826{col 97}{space 3} .1293975
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0835632{col 56}{space 2} .0349156{col 67}{space 1}    2.39{col 76}{space 3}0.021{col 84}{space 4} .0133221{col 97}{space 3} .1538043
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0734654{col 56}{space 2}  .036269{col 67}{space 1}    2.03{col 76}{space 3}0.049{col 84}{space 4} .0005016{col 97}{space 3} .1464291
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0906339{col 56}{space 2} .0402229{col 67}{space 1}    2.25{col 76}{space 3}0.029{col 84}{space 4} .0097159{col 97}{space 3} .1715519
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0672443{col 56}{space 2} .0361191{col 67}{space 1}    1.86{col 76}{space 3}0.069{col 84}{space 4}-.0054181{col 97}{space 3} .1399066
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0161734{col 56}{space 2} .0542055{col 67}{space 1}    0.30{col 76}{space 3}0.767{col 84}{space 4} -.092874{col 97}{space 3} .1252208
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0374402{col 56}{space 2} .0392811{col 67}{space 1}    0.95{col 76}{space 3}0.345{col 84}{space 4}-.0415831{col 97}{space 3} .1164635
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}   .01417{col 56}{space 2} .0270867{col 67}{space 1}    0.52{col 76}{space 3}0.603{col 84}{space 4}-.0403214{col 97}{space 3} .0686614
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0013472{col 56}{space 2} .0005941{col 67}{space 1}   -2.27{col 76}{space 3}0.028{col 84}{space 4}-.0025423{col 97}{space 3} -.000152
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0612334{col 56}{space 2} .0313381{col 67}{space 1}   -1.95{col 76}{space 3}0.057{col 84}{space 4}-.1242776{col 97}{space 3} .0018108
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1141083{col 56}{space 2} .0234279{col 67}{space 1}   -4.87{col 76}{space 3}0.000{col 84}{space 4}-.1612392{col 97}{space 3}-.0669773
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0145929{col 56}{space 2} .0301567{col 67}{space 1}   -0.48{col 76}{space 3}0.631{col 84}{space 4}-.0752603{col 97}{space 3} .0460746
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .1002785{col 56}{space 2} .0337055{col 67}{space 1}    2.98{col 76}{space 3}0.005{col 84}{space 4} .0324718{col 97}{space 3} .1680851
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0534977{col 56}{space 2}  .032456{col 67}{space 1}   -1.65{col 76}{space 3}0.106{col 84}{space 4}-.1187907{col 97}{space 3} .0117954
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0147513{col 56}{space 2} .0294123{col 67}{space 1}    0.50{col 76}{space 3}0.618{col 84}{space 4}-.0444186{col 97}{space 3} .0739212
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2208  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2913137{col 56}{space 2} .0733039{col 67}{space 1}    3.97{col 76}{space 3}0.000{col 84}{space 4} .1438454{col 97}{space 3} .4387821
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1996}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,464    .2666396    .4422921          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(47) = {res}    0.2063
{col 25}{txt}Prob>|t| = {res}    0.8729

95%{txt} confidence set for null hypothesis expression: {res}[−.1064, .1217]
{err}1996

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2208 {c |}{res}      2,464      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,464      100.00
{txt}{p 0 6 2}note: {bf:2382.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    23,428
                                                {txt}F(16, 49)         =  {res}    10.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0173
                                                {txt}Root MSE          =    {res} .45665

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0116742{col 56}{space 2} .0227518{col 67}{space 1}   -0.51{col 76}{space 3}0.610{col 84}{space 4}-.0573956{col 97}{space 3} .0340472
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .1142138{col 56}{space 2} .0496759{col 67}{space 1}    2.30{col 76}{space 3}0.026{col 84}{space 4} .0143863{col 97}{space 3} .2140413
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0421223{col 56}{space 2} .0203331{col 67}{space 1}   -2.07{col 76}{space 3}0.044{col 84}{space 4}-.0829832{col 97}{space 3}-.0012613
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0678205{col 56}{space 2} .0237995{col 67}{space 1}   -2.85{col 76}{space 3}0.006{col 84}{space 4}-.1156474{col 97}{space 3}-.0199937
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0667023{col 56}{space 2} .0230434{col 67}{space 1}   -2.89{col 76}{space 3}0.006{col 84}{space 4}-.1130097{col 97}{space 3}-.0203948
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0511051{col 56}{space 2} .0243796{col 67}{space 1}   -2.10{col 76}{space 3}0.041{col 84}{space 4}-.1000978{col 97}{space 3}-.0021124
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0298871{col 56}{space 2} .0231938{col 67}{space 1}   -1.29{col 76}{space 3}0.204{col 84}{space 4}-.0764968{col 97}{space 3} .0167225
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} -.118878{col 56}{space 2} .0575858{col 67}{space 1}   -2.06{col 76}{space 3}0.044{col 84}{space 4}-.2346011{col 97}{space 3} -.003155
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0109725{col 56}{space 2} .0070688{col 67}{space 1}   -1.55{col 76}{space 3}0.127{col 84}{space 4}-.0251778{col 97}{space 3} .0032328
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0016059{col 56}{space 2} .0003312{col 67}{space 1}    4.85{col 76}{space 3}0.000{col 84}{space 4} .0009404{col 97}{space 3} .0022714
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0218306{col 56}{space 2} .0131635{col 67}{space 1}    1.66{col 76}{space 3}0.104{col 84}{space 4}-.0046225{col 97}{space 3} .0482837
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0406847{col 56}{space 2} .0181552{col 67}{space 1}    2.24{col 76}{space 3}0.030{col 84}{space 4} .0042005{col 97}{space 3}  .077169
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0402822{col 56}{space 2} .0139048{col 67}{space 1}   -2.90{col 76}{space 3}0.006{col 84}{space 4}-.0682248{col 97}{space 3}-.0123395
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0132376{col 56}{space 2} .0121337{col 67}{space 1}    1.09{col 76}{space 3}0.281{col 84}{space 4} -.011146{col 97}{space 3} .0376211
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0090594{col 56}{space 2} .0148138{col 67}{space 1}   -0.61{col 76}{space 3}0.544{col 84}{space 4}-.0388288{col 97}{space 3} .0207099
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0133886{col 56}{space 2} .0143251{col 67}{space 1}    0.93{col 76}{space 3}0.355{col 84}{space 4}-.0153987{col 97}{space 3} .0421759
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2382  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1512645{col 56}{space 2} .0621079{col 67}{space 1}    2.44{col 76}{space 3}0.019{col 84}{space 4}  .026454{col 97}{space 3}  .276075
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2000}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     23,428    .3051477    .4604798          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}   -0.5131
{col 25}{txt}Prob>|t| = {res}    0.6857

95%{txt} confidence set for null hypothesis expression: {res}[−.1676, .09338]
{err}2000

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2382 {c |}{res}     23,428      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     23,428      100.00
{txt}{p 0 6 2}note: {bf:2555.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    23,526
                                                {txt}F(16, 49)         =  {res}    17.72
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0241
                                                {txt}Root MSE          =    {res} .43981

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0099094{col 56}{space 2} .0046959{col 67}{space 1}    2.11{col 76}{space 3}0.040{col 84}{space 4} .0004727{col 97}{space 3} .0193461
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .0714226{col 56}{space 2} .0457779{col 67}{space 1}    1.56{col 76}{space 3}0.125{col 84}{space 4}-.0205715{col 97}{space 3} .1634166
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0308758{col 56}{space 2} .0217334{col 67}{space 1}   -1.42{col 76}{space 3}0.162{col 84}{space 4}-.0745506{col 97}{space 3}  .012799
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0621061{col 56}{space 2} .0242484{col 67}{space 1}   -2.56{col 76}{space 3}0.014{col 84}{space 4}-.1108351{col 97}{space 3}-.0133771
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0536619{col 56}{space 2} .0265187{col 67}{space 1}   -2.02{col 76}{space 3}0.048{col 84}{space 4}-.1069532{col 97}{space 3}-.0003706
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0430488{col 56}{space 2} .0251186{col 67}{space 1}   -1.71{col 76}{space 3}0.093{col 84}{space 4}-.0935266{col 97}{space 3}  .007429
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0324154{col 56}{space 2} .0350735{col 67}{space 1}   -0.92{col 76}{space 3}0.360{col 84}{space 4}-.1028983{col 97}{space 3} .0380674
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0732611{col 56}{space 2} .0655827{col 67}{space 1}   -1.12{col 76}{space 3}0.269{col 84}{space 4}-.2050544{col 97}{space 3} .0585322
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0491012{col 56}{space 2} .0064025{col 67}{space 1}   -7.67{col 76}{space 3}0.000{col 84}{space 4}-.0619675{col 97}{space 3} -.036235
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0024208{col 56}{space 2} .0004034{col 67}{space 1}    6.00{col 76}{space 3}0.000{col 84}{space 4} .0016101{col 97}{space 3} .0032315
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0234579{col 56}{space 2} .0134668{col 67}{space 1}    1.74{col 76}{space 3}0.088{col 84}{space 4}-.0036047{col 97}{space 3} .0505205
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0396031{col 56}{space 2} .0187039{col 67}{space 1}    2.12{col 76}{space 3}0.039{col 84}{space 4} .0020161{col 97}{space 3} .0771901
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} -.037877{col 56}{space 2} .0120167{col 67}{space 1}   -3.15{col 76}{space 3}0.003{col 84}{space 4}-.0620254{col 97}{space 3}-.0137287
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0105861{col 56}{space 2} .0118701{col 67}{space 1}    0.89{col 76}{space 3}0.377{col 84}{space 4}-.0132677{col 97}{space 3} .0344399
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0029407{col 56}{space 2} .0146413{col 67}{space 1}   -0.20{col 76}{space 3}0.842{col 84}{space 4}-.0323634{col 97}{space 3} .0264821
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0301341{col 56}{space 2} .0125062{col 67}{space 1}    2.41{col 76}{space 3}0.020{col 84}{space 4} .0050019{col 97}{space 3} .0552664
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2555  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1208433{col 56}{space 2}  .057402{col 67}{space 1}    2.11{col 76}{space 3}0.040{col 84}{space 4} .0054896{col 97}{space 3}  .236197
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2004}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     23,526    .2721245    .4450631          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}    2.1102
{col 25}{txt}Prob>|t| = {res}    0.0971

95%{txt} confidence set for null hypothesis expression: {res}[−.006525, .04478]
{err}2004

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2555 {c |}{res}     23,526      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     23,526      100.00
{txt}{p 0 6 2}note: {bf:2750.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    17,659
                                                {txt}F(16, 49)         =  {res}     3.91
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0031
                                                {txt}Root MSE          =    {res} .46158

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0006035{col 56}{space 2} .0016623{col 67}{space 1}    0.36{col 76}{space 3}0.718{col 84}{space 4}-.0027369{col 97}{space 3} .0039439
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0006909{col 56}{space 2}  .018904{col 67}{space 1}   -0.04{col 76}{space 3}0.971{col 84}{space 4}-.0386799{col 97}{space 3} .0372981
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0099381{col 56}{space 2} .0193394{col 67}{space 1}    0.51{col 76}{space 3}0.610{col 84}{space 4}-.0289258{col 97}{space 3}  .048802
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0029263{col 56}{space 2}  .019134{col 67}{space 1}    0.15{col 76}{space 3}0.879{col 84}{space 4}-.0355248{col 97}{space 3} .0413774
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0077673{col 56}{space 2} .0238425{col 67}{space 1}    0.33{col 76}{space 3}0.746{col 84}{space 4} -.040146{col 97}{space 3} .0556805
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0228771{col 56}{space 2} .0194647{col 67}{space 1}   -1.18{col 76}{space 3}0.246{col 84}{space 4}-.0619929{col 97}{space 3} .0162387
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0259601{col 56}{space 2} .0273484{col 67}{space 1}    0.95{col 76}{space 3}0.347{col 84}{space 4}-.0289986{col 97}{space 3} .0809187
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0301797{col 56}{space 2} .0282385{col 67}{space 1}   -1.07{col 76}{space 3}0.290{col 84}{space 4}-.0869272{col 97}{space 3} .0265677
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0008884{col 56}{space 2} .0078324{col 67}{space 1}    0.11{col 76}{space 3}0.910{col 84}{space 4}-.0148515{col 97}{space 3} .0166282
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0001447{col 56}{space 2} .0003388{col 67}{space 1}   -0.43{col 76}{space 3}0.671{col 84}{space 4}-.0008255{col 97}{space 3} .0005361
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0338663{col 56}{space 2} .0112725{col 67}{space 1}   -3.00{col 76}{space 3}0.004{col 84}{space 4}-.0565192{col 97}{space 3}-.0112133
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0338721{col 56}{space 2} .0174356{col 67}{space 1}   -1.94{col 76}{space 3}0.058{col 84}{space 4}-.0689102{col 97}{space 3}  .001166
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}  .046704{col 56}{space 2} .0170365{col 67}{space 1}    2.74{col 76}{space 3}0.009{col 84}{space 4} .0124678{col 97}{space 3} .0809403
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0055979{col 56}{space 2} .0138553{col 67}{space 1}   -0.40{col 76}{space 3}0.688{col 84}{space 4}-.0334412{col 97}{space 3} .0222454
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0083109{col 56}{space 2}  .019712{col 67}{space 1}    0.42{col 76}{space 3}0.675{col 84}{space 4}-.0313018{col 97}{space 3} .0479237
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0145547{col 56}{space 2} .0130315{col 67}{space 1}   -1.12{col 76}{space 3}0.269{col 84}{space 4}-.0407424{col 97}{space 3} .0116331
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2750  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3339148{col 56}{space 2} .0368963{col 67}{space 1}    9.05{col 76}{space 3}0.000{col 84}{space 4} .2597689{col 97}{space 3} .4080607
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2008}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     17,659    .3089643    .4620795          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}    0.3631
{col 25}{txt}Prob>|t| = {res}    0.7467

95%{txt} confidence set for null hypothesis expression: {res}[−.01272, .01763]
{err}2008

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2750 {c |}{res}     17,659      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     17,659      100.00
{txt}{p 0 6 2}note: {bf:2909.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,464
                                                {txt}F(16, 46)         =  {res}     4.61
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0162
                                                {txt}Root MSE          =    {res} .43642

{txt}{ralign 108:(Std. err. adjusted for {res:47} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0037954{col 56}{space 2} .0245207{col 67}{space 1}    0.15{col 76}{space 3}0.878{col 84}{space 4}-.0455622{col 97}{space 3} .0531529
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .0497755{col 56}{space 2} .0489606{col 67}{space 1}    1.02{col 76}{space 3}0.315{col 84}{space 4}-.0487771{col 97}{space 3} .1483282
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0068258{col 56}{space 2} .0561156{col 67}{space 1}   -0.12{col 76}{space 3}0.904{col 84}{space 4}-.1197806{col 97}{space 3} .1061289
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0379036{col 56}{space 2} .0518927{col 67}{space 1}   -0.73{col 76}{space 3}0.469{col 84}{space 4}-.1423581{col 97}{space 3} .0665509
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0533409{col 56}{space 2} .0615933{col 67}{space 1}   -0.87{col 76}{space 3}0.391{col 84}{space 4}-.1773217{col 97}{space 3} .0706399
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0136031{col 56}{space 2} .0537942{col 67}{space 1}   -0.25{col 76}{space 3}0.801{col 84}{space 4}-.1218852{col 97}{space 3} .0946789
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0268963{col 56}{space 2} .0600875{col 67}{space 1}   -0.45{col 76}{space 3}0.657{col 84}{space 4}-.1478461{col 97}{space 3} .0940535
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0095634{col 56}{space 2} .0529333{col 67}{space 1}   -0.18{col 76}{space 3}0.857{col 84}{space 4}-.1161126{col 97}{space 3} .0969858
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0062222{col 56}{space 2} .0202483{col 67}{space 1}   -0.31{col 76}{space 3}0.760{col 84}{space 4}-.0469798{col 97}{space 3} .0345355
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0000619{col 56}{space 2} .0008487{col 67}{space 1}   -0.07{col 76}{space 3}0.942{col 84}{space 4}-.0017702{col 97}{space 3} .0016463
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0589238{col 56}{space 2} .0195267{col 67}{space 1}   -3.02{col 76}{space 3}0.004{col 84}{space 4} -.098229{col 97}{space 3}-.0196185
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0663605{col 56}{space 2} .0283552{col 67}{space 1}   -2.34{col 76}{space 3}0.024{col 84}{space 4}-.1234367{col 97}{space 3}-.0092844
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0005067{col 56}{space 2} .0243787{col 67}{space 1}    0.02{col 76}{space 3}0.984{col 84}{space 4}-.0485651{col 97}{space 3} .0495785
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0659941{col 56}{space 2} .0353379{col 67}{space 1}    1.87{col 76}{space 3}0.068{col 84}{space 4}-.0051374{col 97}{space 3} .1371256
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0457111{col 56}{space 2} .0473329{col 67}{space 1}   -0.97{col 76}{space 3}0.339{col 84}{space 4}-.1409874{col 97}{space 3} .0495652
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0448772{col 56}{space 2} .0366536{col 67}{space 1}    1.22{col 76}{space 3}0.227{col 84}{space 4}-.0289025{col 97}{space 3}  .118657
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2909  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2642673{col 56}{space 2} .0731461{col 67}{space 1}    3.61{col 76}{space 3}0.001{col 84}{space 4} .1170318{col 97}{space 3} .4115028
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2011}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,464    .2597403     .438581          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(46) = {res}    0.1548
{col 25}{txt}Prob>|t| = {res}    0.9089

95%{txt} confidence set for null hypothesis expression: {res}[−.2827, .1835]
{err}2011

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2909 {c |}{res}      2,464      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,464      100.00
{txt}{p 0 6 2}note: {bf:3114.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,476
                                                {txt}F(16, 49)         =  {res}     9.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0545
                                                {txt}Root MSE          =    {res} .34864

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0005834{col 56}{space 2} .0153091{col 67}{space 1}    0.04{col 76}{space 3}0.970{col 84}{space 4}-.0301815{col 97}{space 3} .0313483
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .1019343{col 56}{space 2} .0394606{col 67}{space 1}    2.58{col 76}{space 3}0.013{col 84}{space 4} .0226353{col 97}{space 3} .1812332
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0512465{col 56}{space 2} .0481471{col 67}{space 1}   -1.06{col 76}{space 3}0.292{col 84}{space 4}-.1480018{col 97}{space 3} .0455087
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0304686{col 56}{space 2} .0447554{col 67}{space 1}   -0.68{col 76}{space 3}0.499{col 84}{space 4}-.1204079{col 97}{space 3} .0594707
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0182859{col 56}{space 2} .0450863{col 67}{space 1}   -0.41{col 76}{space 3}0.687{col 84}{space 4}-.1088903{col 97}{space 3} .0723184
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0820316{col 56}{space 2} .0411998{col 67}{space 1}   -1.99{col 76}{space 3}0.052{col 84}{space 4}-.1648258{col 97}{space 3} .0007625
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0758912{col 56}{space 2} .0484463{col 67}{space 1}   -1.57{col 76}{space 3}0.124{col 84}{space 4}-.1732478{col 97}{space 3} .0214654
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} -.044447{col 56}{space 2} .0653411{col 67}{space 1}   -0.68{col 76}{space 3}0.500{col 84}{space 4}-.1757548{col 97}{space 3} .0868608
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0212643{col 56}{space 2}  .013539{col 67}{space 1}   -1.57{col 76}{space 3}0.123{col 84}{space 4} -.048472{col 97}{space 3} .0059434
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0032927{col 56}{space 2} .0007571{col 67}{space 1}    4.35{col 76}{space 3}0.000{col 84}{space 4} .0017712{col 97}{space 3} .0048142
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} -.021956{col 56}{space 2} .0235533{col 67}{space 1}   -0.93{col 76}{space 3}0.356{col 84}{space 4}-.0692882{col 97}{space 3} .0253763
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0197069{col 56}{space 2} .0222987{col 67}{space 1}   -0.88{col 76}{space 3}0.381{col 84}{space 4}-.0645178{col 97}{space 3}  .025104
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0173301{col 56}{space 2} .0197033{col 67}{space 1}   -0.88{col 76}{space 3}0.383{col 84}{space 4}-.0569253{col 97}{space 3} .0222651
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0088849{col 56}{space 2} .0212097{col 67}{space 1}    0.42{col 76}{space 3}0.677{col 84}{space 4}-.0337375{col 97}{space 3} .0515074
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0576621{col 56}{space 2} .0332056{col 67}{space 1}    1.74{col 76}{space 3}0.089{col 84}{space 4}-.0090671{col 97}{space 3} .1243912
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0040126{col 56}{space 2} .0322443{col 67}{space 1}    0.12{col 76}{space 3}0.901{col 84}{space 4}-.0607847{col 97}{space 3} .0688098
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3114  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}-.0490566{col 56}{space 2} .0698356{col 67}{space 1}   -0.70{col 76}{space 3}0.486{col 84}{space 4}-.1893965{col 97}{space 3} .0912833
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2015}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,476    .1502423     .357381          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}    0.0381
{col 25}{txt}Prob>|t| = {res}    0.9810

95%{txt} confidence set for null hypothesis expression: {res}[−.06235, .1176]
{err}2015

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3114 {c |}{res}      2,476      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,476      100.00
{txt}{p 0 6 2}note: {bf:3134.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,475
                                                {txt}F(16, 48)         =  {res}     7.10
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0512
                                                {txt}Root MSE          =    {res} .36896

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0333067{col 56}{space 2}  .010214{col 67}{space 1}    3.26{col 76}{space 3}0.002{col 84}{space 4} .0127701{col 97}{space 3} .0538433
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .2545608{col 56}{space 2}  .170173{col 67}{space 1}    1.50{col 76}{space 3}0.141{col 84}{space 4}-.0875949{col 97}{space 3} .5967164
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} -.038527{col 56}{space 2}  .057443{col 67}{space 1}   -0.67{col 76}{space 3}0.506{col 84}{space 4}-.1540238{col 97}{space 3} .0769699
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0491506{col 56}{space 2} .0479357{col 67}{space 1}   -1.03{col 76}{space 3}0.310{col 84}{space 4}-.1455319{col 97}{space 3} .0472306
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0420343{col 56}{space 2} .0511432{col 67}{space 1}   -0.82{col 76}{space 3}0.415{col 84}{space 4}-.1448645{col 97}{space 3} .0607959
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0435096{col 56}{space 2} .0498191{col 67}{space 1}   -0.87{col 76}{space 3}0.387{col 84}{space 4}-.1436775{col 97}{space 3} .0566584
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0533806{col 56}{space 2}  .064864{col 67}{space 1}   -0.82{col 76}{space 3}0.415{col 84}{space 4}-.1837985{col 97}{space 3} .0770373
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0848571{col 56}{space 2} .0687892{col 67}{space 1}   -1.23{col 76}{space 3}0.223{col 84}{space 4}-.2231671{col 97}{space 3} .0534528
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} -.040001{col 56}{space 2} .0138339{col 67}{space 1}   -2.89{col 76}{space 3}0.006{col 84}{space 4}-.0678159{col 97}{space 3}-.0121861
{txt}{space 39}age {c |}{col 44}{res}{space 2}   .00248{col 56}{space 2} .0007242{col 67}{space 1}    3.42{col 76}{space 3}0.001{col 84}{space 4} .0010238{col 97}{space 3} .0039361
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0013469{col 56}{space 2} .0206649{col 67}{space 1}   -0.07{col 76}{space 3}0.948{col 84}{space 4}-.0428965{col 97}{space 3} .0402027
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0036761{col 56}{space 2} .0244873{col 67}{space 1}    0.15{col 76}{space 3}0.881{col 84}{space 4}-.0455589{col 97}{space 3} .0529112
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0093757{col 56}{space 2} .0184897{col 67}{space 1}   -0.51{col 76}{space 3}0.614{col 84}{space 4}-.0465517{col 97}{space 3} .0278004
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0688928{col 56}{space 2} .0252326{col 67}{space 1}    2.73{col 76}{space 3}0.009{col 84}{space 4} .0181592{col 97}{space 3} .1196263
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0378894{col 56}{space 2} .0275328{col 67}{space 1}    1.38{col 76}{space 3}0.175{col 84}{space 4} -.017469{col 97}{space 3} .0932478
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .1146759{col 56}{space 2} .0501473{col 67}{space 1}    2.29{col 76}{space 3}0.027{col 84}{space 4} .0138479{col 97}{space 3} .2155038
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3134  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .0213036{col 56}{space 2}   .06263{col 67}{space 1}    0.34{col 76}{space 3}0.735{col 84}{space 4}-.1046225{col 97}{space 3} .1472296
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2016}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,475    .1721212     .377562          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(48) = {res}    3.2609
{col 25}{txt}Prob>|t| = {res}    0.8879

95%{txt} confidence set for null hypothesis expression: {res}[−.386, .9831]
{err}2016

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3134 {c |}{res}      2,475      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,475      100.00
{txt}{p 0 6 2}note: {bf:3242.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    15,784
                                                {txt}F(16, 49)         =  {res}    20.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0156
                                                {txt}Root MSE          =    {res} .38667

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0172296{col 56}{space 2} .0223284{col 67}{space 1}   -0.77{col 76}{space 3}0.444{col 84}{space 4}-.0621003{col 97}{space 3}  .027641
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}  .034721{col 56}{space 2} .0612433{col 67}{space 1}    0.57{col 76}{space 3}0.573{col 84}{space 4} -.088352{col 97}{space 3}  .157794
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0136792{col 56}{space 2} .0163348{col 67}{space 1}    0.84{col 76}{space 3}0.406{col 84}{space 4}-.0191469{col 97}{space 3} .0465052
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}   .00221{col 56}{space 2} .0189795{col 67}{space 1}    0.12{col 76}{space 3}0.908{col 84}{space 4}-.0359309{col 97}{space 3} .0403508
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0078752{col 56}{space 2} .0198936{col 67}{space 1}    0.40{col 76}{space 3}0.694{col 84}{space 4}-.0321025{col 97}{space 3} .0478529
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0066215{col 56}{space 2} .0202344{col 67}{space 1}    0.33{col 76}{space 3}0.745{col 84}{space 4}-.0340411{col 97}{space 3}  .047284
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0072847{col 56}{space 2} .0254367{col 67}{space 1}    0.29{col 76}{space 3}0.776{col 84}{space 4}-.0438323{col 97}{space 3} .0584018
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0320071{col 56}{space 2} .0209291{col 67}{space 1}    1.53{col 76}{space 3}0.133{col 84}{space 4}-.0100514{col 97}{space 3} .0740656
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0055759{col 56}{space 2} .0052607{col 67}{space 1}   -1.06{col 76}{space 3}0.294{col 84}{space 4}-.0161476{col 97}{space 3} .0049959
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0012729{col 56}{space 2} .0002667{col 67}{space 1}    4.77{col 76}{space 3}0.000{col 84}{space 4} .0007369{col 97}{space 3} .0018088
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0344486{col 56}{space 2}  .009589{col 67}{space 1}   -3.59{col 76}{space 3}0.001{col 84}{space 4}-.0537184{col 97}{space 3}-.0151788
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0395573{col 56}{space 2} .0092647{col 67}{space 1}   -4.27{col 76}{space 3}0.000{col 84}{space 4}-.0581754{col 97}{space 3}-.0209393
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0433758{col 56}{space 2} .0105982{col 67}{space 1}    4.09{col 76}{space 3}0.000{col 84}{space 4} .0220779{col 97}{space 3} .0646737
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0435493{col 56}{space 2} .0133198{col 67}{space 1}    3.27{col 76}{space 3}0.002{col 84}{space 4} .0167822{col 97}{space 3} .0703165
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0037442{col 56}{space 2} .0144017{col 67}{space 1}    0.26{col 76}{space 3}0.796{col 84}{space 4}-.0251972{col 97}{space 3} .0326856
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0080239{col 56}{space 2}   .01239{col 67}{space 1}   -0.65{col 76}{space 3}0.520{col 84}{space 4}-.0329225{col 97}{space 3} .0168748
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3242  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1105953{col 56}{space 2} .0282718{col 67}{space 1}    3.91{col 76}{space 3}0.000{col 84}{space 4} .0537811{col 97}{space 3} .1674096
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:pre_m}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2019}"

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     15,784     .186518    .3895364          0          1

{txt}Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}   -0.7716
{col 25}{txt}Prob>|t| = {res}    0.7347

95%{txt} confidence set for null hypothesis expression: {res}[−.28, .5215]
{err}2019

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3242 {c |}{res}     15,784      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     15,784      100.00
{txt}
{com}. 
. * Step 2: Save the Results to a New Dataset
. 
. * Double-check the matrix contents before conversion
. matrix list bootstrap_results
{res}
{txt}bootstrap_results[31,3]
             c1          c2          c3
 r1 {res} -.00556427  -.16970287   .16465354
{txt} r2 {res}          .           .           .
{txt} r3 {res}          .           .           .
{txt} r4 {res}          .           .           .
{txt} r5 {res} -.06297645  -.20607019   .00274503
{txt} r6 {res}          .           .           .
{txt} r7 {res}          .           .           .
{txt} r8 {res}  .00919008  -.10637651   .12166089
{txt} r9 {res}          .           .           .
{txt}r10 {res}          .           .           .
{txt}r11 {res}          .           .           .
{txt}r12 {res} -.01167418  -.16761559   .09338201
{txt}r13 {res}          .           .           .
{txt}r14 {res}          .           .           .
{txt}r15 {res}          .           .           .
{txt}r16 {res}  .00990942  -.00652482   .04477574
{txt}r17 {res}          .           .           .
{txt}r18 {res}          .           .           .
{txt}r19 {res}          .           .           .
{txt}r20 {res}  .00060351  -.01272372   .01762936
{txt}r21 {res}          .           .           .
{txt}r22 {res}          .           .           .
{txt}r23 {res}  .00379536  -.28272092   .18345687
{txt}r24 {res}          .           .           .
{txt}r25 {res}          .           .           .
{txt}r26 {res}          .           .           .
{txt}r27 {res}  .00058342  -.06235162   .11761612
{txt}r28 {res}  .03330668   -.3859585   .98307294
{txt}r29 {res}          .           .           .
{txt}r30 {res}          .           .           .
{txt}r31 {res} -.01722963  -.28004358   .52151112
{reset}
{com}. 
. * Save the results to a new dataset
. svmat double bootstrap_results, names(col)
{txt}
{com}. rename c1 top_prizes_coefficient
{res}{txt}
{com}. rename c2 top_prizes_ci_lower
{res}{txt}
{com}. rename c3 top_prizes_ci_upper
{res}{txt}
{com}. 
. * Step 3: Clean the Dataset and Calculate Standard Errors
. 
. * Drop rows where the coefficients or confidence intervals are missing
. drop if missing(top_prizes_coefficient) & missing(top_prizes_ci_lower) & missing(top_prizes_ci_upper) 
{txt}(105,860 observations deleted)

{com}. 
. * Keep only the relevant variables for the meta-analysis
. keep top_prizes_coefficient top_prizes_ci_lower top_prizes_ci_upper
{txt}
{com}. 
. * Generate a new year variable for the meta-analysis
. gen year = _n   // Adjust the starting year if necessary
{txt}
{com}. 
. replace year = 1989 if year == 1 
{txt}(1 real change made)

{com}. replace year = 1990 if year == 2
{txt}(1 real change made)

{com}. replace year =  1993 if year == 3
{txt}(1 real change made)

{com}. replace year = 1996 if year == 4
{txt}(1 real change made)

{com}. replace year =  2000 if year == 5
{txt}(1 real change made)

{com}. replace year =  2004 if year == 6
{txt}(1 real change made)

{com}. replace year = 2008 if year == 7
{txt}(1 real change made)

{com}. replace year =  2011 if year == 8
{txt}(1 real change made)

{com}. replace year = 2015 if year == 9
{txt}(1 real change made)

{com}. replace year = 2019 if year == 10
{txt}(1 real change made)

{com}. 
. * Calculate standard errors from the confidence intervals
. gen top_prizes_se = (top_prizes_ci_upper - top_prizes_ci_lower) / (2 * 1.96)
{txt}
{com}. 
. * Step 4: Meta-Analysis for top_prizes_gdp and expenditure_gdp
. 
. * Ensure that the year variable is correctly set and does not have duplicate values
. sort year
{txt}
{com}. 
. *** Pooled:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}10
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}    10
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             1989{col 19}{c |}{res}{space 6}   -0.006{col 35}{space 3}   -0.173{col 47}{space 3}    0.162{col 59}{space 5} 0.57
{col 1}{txt}             1990{col 19}{c |}{res}{space 6}   -0.063{col 35}{space 3}   -0.167{col 47}{space 3}    0.041{col 59}{space 5} 1.46
{col 1}{txt}             1993{col 19}{c |}{res}{space 6}    0.009{col 35}{space 3}   -0.105{col 47}{space 3}    0.123{col 59}{space 5} 1.23
{col 1}{txt}             1996{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.142{col 47}{space 3}    0.119{col 59}{space 5} 0.94
{col 1}{txt}             2000{col 19}{c |}{res}{space 6}    0.010{col 35}{space 3}   -0.016{col 47}{space 3}    0.036{col 59}{space 5}24.23
{col 1}{txt}             2004{col 19}{c |}{res}{space 6}    0.001{col 35}{space 3}   -0.015{col 47}{space 3}    0.016{col 59}{space 5}69.18
{col 1}{txt}             2008{col 19}{c |}{res}{space 6}    0.004{col 35}{space 3}   -0.229{col 47}{space 3}    0.237{col 59}{space 5} 0.29
{col 1}{txt}             2011{col 19}{c |}{res}{space 6}    0.001{col 35}{space 3}   -0.089{col 47}{space 3}    0.091{col 59}{space 5} 1.97
{col 1}{txt}             2015{col 19}{c |}{res}{space 6}    0.033{col 35}{space 3}   -0.651{col 47}{space 3}    0.718{col 59}{space 5} 0.03
{col 1}{txt}             2019{col 19}{c |}{res}{space 6}   -0.017{col 35}{space 3}   -0.418{col 47}{space 3}    0.384{col 59}{space 5} 0.10
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.002{col 35}{space 3}   -0.011{col 47}{space 3}    0.015
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.29{txt}{col 50}Prob > |z| = {res}0.7697
{txt}Test of homogeneity: Q = chi2({res}9{txt}) = {res}1.97{txt}{col 52}Prob > Q = {res}0.9920
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_term_prizes_pooled_premonth.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_term_prizes_pooled_premonth.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. preserve 
{txt}
{com}. 
. keep if year<2010
{txt}(3 observations deleted)

{com}. 
. *** Pre:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}7
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}     7
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.01
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             1989{col 19}{c |}{res}{space 6}   -0.006{col 35}{space 3}   -0.173{col 47}{space 3}    0.162{col 59}{space 5} 0.58
{col 1}{txt}             1990{col 19}{c |}{res}{space 6}   -0.063{col 35}{space 3}   -0.167{col 47}{space 3}    0.041{col 59}{space 5} 1.49
{col 1}{txt}             1993{col 19}{c |}{res}{space 6}    0.009{col 35}{space 3}   -0.105{col 47}{space 3}    0.123{col 59}{space 5} 1.25
{col 1}{txt}             1996{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.142{col 47}{space 3}    0.119{col 59}{space 5} 0.96
{col 1}{txt}             2000{col 19}{c |}{res}{space 6}    0.010{col 35}{space 3}   -0.016{col 47}{space 3}    0.036{col 59}{space 5}24.75
{col 1}{txt}             2004{col 19}{c |}{res}{space 6}    0.001{col 35}{space 3}   -0.015{col 47}{space 3}    0.016{col 59}{space 5}70.66
{col 1}{txt}             2008{col 19}{c |}{res}{space 6}    0.004{col 35}{space 3}   -0.229{col 47}{space 3}    0.237{col 59}{space 5} 0.30
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.002{col 35}{space 3}   -0.011{col 47}{space 3}    0.015
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.29{txt}{col 50}Prob > |z| = {res}0.7681
{txt}Test of homogeneity: Q = chi2({res}6{txt}) = {res}1.95{txt}{col 52}Prob > Q = {res}0.9241
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_term_prizes_pre_premonth.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_term_prizes_pre_premonth.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. restore
{txt}
{com}. 
. preserve 
{txt}
{com}. 
. keep if year>=2010
{txt}(7 observations deleted)

{com}. 
. **** Post:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}3
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}     3
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             2011{col 19}{c |}{res}{space 6}    0.001{col 35}{space 3}   -0.089{col 47}{space 3}    0.091{col 59}{space 5}93.66
{col 1}{txt}             2015{col 19}{c |}{res}{space 6}    0.033{col 35}{space 3}   -0.651{col 47}{space 3}    0.718{col 59}{space 5} 1.62
{col 1}{txt}             2019{col 19}{c |}{res}{space 6}   -0.017{col 35}{space 3}   -0.418{col 47}{space 3}    0.384{col 59}{space 5} 4.72
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.000{col 35}{space 3}   -0.087{col 47}{space 3}    0.087
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.01{txt}{col 50}Prob > |z| = {res}0.9951
{txt}Test of homogeneity: Q = chi2({res}2{txt}) = {res}0.02{txt}{col 52}Prob > Q = {res}0.9919
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_term_prizes_post_premonth.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_term_prizes_post_premonth.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. * Combine meta-analysis results into a single model -- place holder for meta analyses 
. eststo meta_combined: estadd scalar top_prizes_gdp_term_2 = top_prizes_meta_coef

{txt}added scalar:
e(top_prizes_gdp_term_2) =  {res}.00027199
{txt}
{com}. estadd scalar top_prizes_se = top_prizes_meta_se

{txt}added scalar:
      e(top_prizes_se) =  {res}.04443244
{txt}
{com}. 
. restore
{txt}
{com}. 
. * Step 5: Generate LaTeX Tables. Appendix Table 28
. 
. * Appendix Table 28. Part  1: years 1989 to 2011:
. esttab boots_pre_m_1989 boots_pre_m_1993 boots_pre_m_1996 boots_pre_m_2000 boots_pre_m_2004 boots_pre_m_2008 boots_pre_m_2011 ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table_28_Part1_1989_2011.tex, ///
>     keep(top_prizes_gdp_term_2) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_term_2 "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_28_Part1_1989_2011.tex"'})

{com}.         
. 
. * Appendix Table 28. Part  2: years 2015 to 2019 + meta analyses
. esttab boots_pre_m_2015 boots_pre_m_2016 boots_pre_m_2019 meta_combined meta_combined meta_combined ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table_28_Part2_2015_2019_meta.tex, ///
>     keep(top_prizes_gdp_term_2) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_term_2 "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table_28_Part2_2015_2019_meta.tex"'})

{com}.         
. 
. 
. /* NOTE: Information of the last 3 columns of the table (the meta-analysis in cols 37-39) and 
> WCB confidence sets are manually added to the tables from the Stata output  generated here.
> */
. 
. 
. **----------------------------------------------------------------------------**
. *** APPENDIX TABLE 29: Survey Results Using Lottery Prizes Cumulated over 
. *** Electoral Term, by Year and Estimation Type, Using Our Data. Outcomes 
. *** measured only in the first quarter of the year (Q1)
. **----------------------------------------------------------------------------**
. 
. 
. use "Data/Our_data/20240723_SpanishLottery_Complete_survey.dta", clear 
{txt}(fileNameJDS)

{com}. 
. drop if province==""
{txt}(0 observations deleted)

{com}. encode province, gen(prov_num)
{txt}
{com}. 
. decode fixed_effect_num, gen(fixed)
{txt}
{com}. 
. preserve
{txt}
{com}. 
. *****
. 
. * read electoral data
. use "Data/Our_data/20250531_SpanishLottery_Complete_province.dta", clear 
{txt}
{com}. 
. 
. rename year year1
{res}{txt}
{com}. 
. keep province year1 top_prizes_gdp_term_2 expenditure_gdp_term_2
{txt}
{com}. duplicates drop /*To drop the second 2019 election since prizes are the same */

{p 0 4}{txt}Duplicates in terms of {txt} all variables{p_end}

(50 observations deleted)

{com}. 
. 
. tempfile electoral_yearQ
{txt}
{com}. 
. save `electoral_yearQ'
{txt}{p 0 4 2}
file {bf}
/var/folders/54/7cxl8ny95nb4vs26j1q2yq9r0000gn/T//S_08037.00000j{rm}
saved
as .dta format
{p_end}

{com}. 
. restore /* Re load survey data */
{txt}
{com}. 
. ** Get period with 3 quarters from year t-1 + first quarter of year t. 
. rename year year_original
{res}{txt}
{com}. split fixed, parse(-) limit(1) gen(year)
{res}variable created as string: 
{txt}{col 1}year1

{com}. destring year1, replace
{txt}year1: all characters numeric; {res}replaced {txt}as {res}int
{txt}
{com}. 
. * merge
. merge m:1 year1 province using `electoral_yearQ' 
{res}{txt}{p 0 7 2}
(variable
{bf:year1} was {bf:int}, now {bf:double} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}         744,009
{txt}{col 9}from master{col 30}{res}         743,959{txt}  (_merge==1)
{col 9}from using{col 30}{res}              50{txt}  (_merge==2)

{col 5}Matched{col 30}{res}         385,551{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3 
{txt}(744,009 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. * replace with zeroes 
. replace top_prizes_gdp_term_2=0 if month>3
{txt}(175,311 real changes made)

{com}. replace expenditure_gdp_term_2=0 if month>3
{txt}(229,088 real changes made)

{com}. 
. 
. drop if month>=4
{txt}(229,088 observations deleted)

{com}. 
. * Step 1: Initialize and Loop through All Years
. 
. local regression_names ""
{txt}
{com}. 
. * Initialize the matrix with 10 rows (electoral years) and 3 columns (coefficients, lower bounds, upper bounds)
. matrix bootstrap_results = J(31, 3, .) /*the matrix has 31 rows to solve pb storing results and avoiding conformability error */
{txt}
{com}. 
. foreach y in 1989 1993 1996 2000 2004 2008 2011 2015 2016 2019{c -(}
{txt}  2{com}.     * Run the regression for the current year
.     eststo boots_Q1_`y': reg vote_incumbent top_prizes_gdp_term_2 expenditure_gdp_term_2 $individual_characteristics i.survey if year_original ==`y', cluster(prov_num)
{txt}  3{com}.         
.         * Labels:
.         estadd local SurveyFE "$\checkmark$"
{txt}  4{com}.         estadd local Estimation "OLS"
{txt}  5{com}.         estadd local Data "Ours"
{txt}  6{com}.         estadd local Outcome "Q1"
{txt}  7{com}.         estadd local ProvinceFE "$\times$"
{txt}  8{com}.         estadd local Year "`y'"
{txt}  9{com}.         
.     * Run boottest for top_prizes_gdp
.         boottest top_prizes_gdp_term_2, cluster(prov_num) seed(12345)
{txt} 10{com}.         
.     * Capture confidence sets from the r(CI) matrix (these are WCB):
.         matrix top_prizes_CI = r(CI)
{txt} 11{com}.     local top_prizes_ci_lower = top_prizes_CI[1,1]
{txt} 12{com}.     local top_prizes_ci_upper = top_prizes_CI[1,2]
{txt} 13{com}.                     
.     
.     * Calculate the row number based on the year
.     local row = `y' - 1988  // 1989 starts at row 1, 1993 at row 5, etc.
{txt} 14{com}.     
.     * Store the coefficients and both confidence interval bounds in the matrix
.     matrix bootstrap_results[`row', 1] = _b[top_prizes_gdp_term_2]
{txt} 15{com}.         matrix bootstrap_results[`row', 2] = `top_prizes_ci_lower'
{txt} 16{com}.     matrix bootstrap_results[`row', 3] = `top_prizes_ci_upper'
{txt} 17{com}. 
.             
.     * Add regression name to the list
.     local regression_names "`regression_names' boots_Q1_`y'"
{txt} 18{com}.         
.         dis as error `y'
{txt} 19{com}.         
.         tab survey if e(sample)==1
{txt} 20{com}.         sum vote_incumbent if e(sample)==1
{txt} 21{com}.         
. {c )-}

{txt}Linear regression                               Number of obs     = {res}     7,288
                                                {txt}F(18, 49)         =  {res}     8.29
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0182
                                                {txt}Root MSE          =    {res} .43187

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0163736{col 56}{space 2} .0122351{col 67}{space 1}   -1.34{col 76}{space 3}0.187{col 84}{space 4} -.040961{col 97}{space 3} .0082138
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0867875{col 56}{space 2} .0719479{col 67}{space 1}   -1.21{col 76}{space 3}0.234{col 84}{space 4}-.2313722{col 97}{space 3} .0577973
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0138969{col 56}{space 2} .0353005{col 67}{space 1}    0.39{col 76}{space 3}0.696{col 84}{space 4} -.057042{col 97}{space 3} .0848359
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}  .034926{col 56}{space 2} .0364855{col 67}{space 1}    0.96{col 76}{space 3}0.343{col 84}{space 4}-.0383943{col 97}{space 3} .1082463
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0169798{col 56}{space 2}  .032339{col 67}{space 1}    0.53{col 76}{space 3}0.602{col 84}{space 4}-.0480079{col 97}{space 3} .0819675
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0250922{col 56}{space 2} .0315644{col 67}{space 1}    0.79{col 76}{space 3}0.430{col 84}{space 4}-.0383389{col 97}{space 3} .0885233
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0048215{col 56}{space 2} .0336325{col 67}{space 1}    0.14{col 76}{space 3}0.887{col 84}{space 4}-.0627654{col 97}{space 3} .0724085
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0048818{col 56}{space 2} .0323299{col 67}{space 1}   -0.15{col 76}{space 3}0.881{col 84}{space 4}-.0698513{col 97}{space 3} .0600876
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0363032{col 56}{space 2} .0144833{col 67}{space 1}   -2.51{col 76}{space 3}0.016{col 84}{space 4}-.0654084{col 97}{space 3}-.0071981
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0003834{col 56}{space 2} .0004351{col 67}{space 1}   -0.88{col 76}{space 3}0.383{col 84}{space 4}-.0012577{col 97}{space 3}  .000491
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0766894{col 56}{space 2} .0165401{col 67}{space 1}   -4.64{col 76}{space 3}0.000{col 84}{space 4} -.109928{col 97}{space 3}-.0434508
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1049717{col 56}{space 2} .0198329{col 67}{space 1}   -5.29{col 76}{space 3}0.000{col 84}{space 4}-.1448274{col 97}{space 3}-.0651159
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0569925{col 56}{space 2} .0184514{col 67}{space 1}    3.09{col 76}{space 3}0.003{col 84}{space 4}  .019913{col 97}{space 3}  .094072
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0519635{col 56}{space 2} .0235178{col 67}{space 1}    2.21{col 76}{space 3}0.032{col 84}{space 4} .0047027{col 97}{space 3} .0992244
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0196982{col 56}{space 2} .0205266{col 67}{space 1}    0.96{col 76}{space 3}0.342{col 84}{space 4}-.0215516{col 97}{space 3} .0609479
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} -.004503{col 56}{space 2} .0142634{col 67}{space 1}   -0.32{col 76}{space 3}0.754{col 84}{space 4}-.0331663{col 97}{space 3} .0241604
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}1791  {c |}{col 44}{res}{space 2}-.0318266{col 56}{space 2} .0140874{col 67}{space 1}   -2.26{col 76}{space 3}0.028{col 84}{space 4}-.0601363{col 97}{space 3}-.0035169
{txt}{space 37}1798  {c |}{col 44}{res}{space 2} .0045059{col 56}{space 2} .0128804{col 67}{space 1}    0.35{col 76}{space 3}0.728{col 84}{space 4}-.0213782{col 97}{space 3} .0303899
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3623592{col 56}{space 2} .0729065{col 67}{space 1}    4.97{col 76}{space 3}0.000{col 84}{space 4} .2158481{col 97}{space 3} .5088703
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1989}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}   -1.3382
{col 25}{txt}Prob>|t| = {res}    0.5155

95%{txt} confidence set for null hypothesis expression: {res}[−.1525, .06839]
{err}1989

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1785 {c |}{res}      2,446       33.56       33.56
{txt}       1791 {c |}{res}      2,413       33.11       66.67
{txt}       1798 {c |}{res}      2,429       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,288      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,288    .2539791    .4353157          0          1

{txt}Linear regression                               Number of obs     = {res}     7,384
                                                {txt}F(18, 48)         =  {res}    21.92
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0460
                                                {txt}Root MSE          =    {res} .40914

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0262491{col 56}{space 2} .0201764{col 67}{space 1}    1.30{col 76}{space 3}0.199{col 84}{space 4}-.0143183{col 97}{space 3} .0668165
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} -.042178{col 56}{space 2} .0405048{col 67}{space 1}   -1.04{col 76}{space 3}0.303{col 84}{space 4}-.1236184{col 97}{space 3} .0392623
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0315898{col 56}{space 2} .0280383{col 67}{space 1}    1.13{col 76}{space 3}0.265{col 84}{space 4}-.0247848{col 97}{space 3} .0879645
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0371765{col 56}{space 2} .0291886{col 67}{space 1}    1.27{col 76}{space 3}0.209{col 84}{space 4} -.021511{col 97}{space 3} .0958641
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0323619{col 56}{space 2} .0300545{col 67}{space 1}    1.08{col 76}{space 3}0.287{col 84}{space 4}-.0280667{col 97}{space 3} .0927904
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0040762{col 56}{space 2} .0286898{col 67}{space 1}    0.14{col 76}{space 3}0.888{col 84}{space 4}-.0536085{col 97}{space 3} .0617609
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0046817{col 56}{space 2} .0346913{col 67}{space 1}   -0.13{col 76}{space 3}0.893{col 84}{space 4}-.0744332{col 97}{space 3} .0650698
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0334349{col 56}{space 2} .0275649{col 67}{space 1}   -1.21{col 76}{space 3}0.231{col 84}{space 4}-.0888579{col 97}{space 3}  .021988
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} -.015094{col 56}{space 2} .0112866{col 67}{space 1}   -1.34{col 76}{space 3}0.187{col 84}{space 4}-.0377872{col 97}{space 3} .0075992
{txt}{space 39}age {c |}{col 44}{res}{space 2}  .000771{col 56}{space 2} .0005443{col 67}{space 1}    1.42{col 76}{space 3}0.163{col 84}{space 4}-.0003233{col 97}{space 3} .0018654
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.1074758{col 56}{space 2} .0175502{col 67}{space 1}   -6.12{col 76}{space 3}0.000{col 84}{space 4}-.1427629{col 97}{space 3}-.0721887
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1070569{col 56}{space 2}  .016745{col 67}{space 1}   -6.39{col 76}{space 3}0.000{col 84}{space 4}-.1407249{col 97}{space 3}-.0733888
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0048079{col 56}{space 2} .0146824{col 67}{space 1}    0.33{col 76}{space 3}0.745{col 84}{space 4} -.024713{col 97}{space 3} .0343288
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0725449{col 56}{space 2} .0211608{col 67}{space 1}    3.43{col 76}{space 3}0.001{col 84}{space 4} .0299983{col 97}{space 3} .1150914
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} -.025318{col 56}{space 2} .0170376{col 67}{space 1}   -1.49{col 76}{space 3}0.144{col 84}{space 4}-.0595745{col 97}{space 3} .0089384
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0171479{col 56}{space 2} .0190031{col 67}{space 1}    0.90{col 76}{space 3}0.371{col 84}{space 4}-.0210603{col 97}{space 3} .0553562
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2046  {c |}{col 44}{res}{space 2}-.0108855{col 56}{space 2} .0114651{col 67}{space 1}   -0.95{col 76}{space 3}0.347{col 84}{space 4}-.0339376{col 97}{space 3} .0121665
{txt}{space 37}2050  {c |}{col 44}{res}{space 2} .0590361{col 56}{space 2} .0151835{col 67}{space 1}    3.89{col 76}{space 3}0.000{col 84}{space 4} .0285076{col 97}{space 3} .0895646
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2242867{col 56}{space 2} .0538269{col 67}{space 1}    4.17{col 76}{space 3}0.000{col 84}{space 4} .1160605{col 97}{space 3} .3325129
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1993}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(48) = {res}    1.3010
{col 25}{txt}Prob>|t| = {res}    0.2573

95%{txt} confidence set for null hypothesis expression: {res}[−.04357, .07639]
{err}1993

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2045 {c |}{res}      2,449       33.17       33.17
{txt}       2046 {c |}{res}      2,455       33.25       66.41
{txt}       2050 {c |}{res}      2,480       33.59      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      7,384      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      7,384    .2261647    .4183753          0          1

{txt}Linear regression                               Number of obs     = {res}     4,924
                                                {txt}F(17, 48)         =  {res}    13.00
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0300
                                                {txt}Root MSE          =    {res} .42977

{txt}{ralign 108:(Std. err. adjusted for {res:49} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0358937{col 56}{space 2} .0272822{col 67}{space 1}    1.32{col 76}{space 3}0.195{col 84}{space 4}-.0189609{col 97}{space 3} .0907482
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0386376{col 56}{space 2} .0528714{col 67}{space 1}   -0.73{col 76}{space 3}0.468{col 84}{space 4}-.1449427{col 97}{space 3} .0676674
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0344206{col 56}{space 2} .0271363{col 67}{space 1}    1.27{col 76}{space 3}0.211{col 84}{space 4}-.0201407{col 97}{space 3} .0889818
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0211962{col 56}{space 2} .0287872{col 67}{space 1}    0.74{col 76}{space 3}0.465{col 84}{space 4}-.0366844{col 97}{space 3} .0790769
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0470164{col 56}{space 2} .0316203{col 67}{space 1}    1.49{col 76}{space 3}0.144{col 84}{space 4}-.0165605{col 97}{space 3} .1105932
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0163068{col 56}{space 2} .0265331{col 67}{space 1}    0.61{col 76}{space 3}0.542{col 84}{space 4}-.0370415{col 97}{space 3} .0696551
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0067622{col 56}{space 2} .0391798{col 67}{space 1}   -0.17{col 76}{space 3}0.864{col 84}{space 4}-.0855385{col 97}{space 3} .0720141
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0172231{col 56}{space 2} .0304067{col 67}{space 1}    0.57{col 76}{space 3}0.574{col 84}{space 4}-.0439137{col 97}{space 3} .0783599
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0194519{col 56}{space 2} .0150307{col 67}{space 1}    1.29{col 76}{space 3}0.202{col 84}{space 4}-.0107693{col 97}{space 3} .0496732
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0000878{col 56}{space 2} .0004407{col 67}{space 1}   -0.20{col 76}{space 3}0.843{col 84}{space 4}-.0009739{col 97}{space 3} .0007983
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0857462{col 56}{space 2} .0217481{col 67}{space 1}   -3.94{col 76}{space 3}0.000{col 84}{space 4}-.1294738{col 97}{space 3}-.0420186
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.1188655{col 56}{space 2} .0174387{col 67}{space 1}   -6.82{col 76}{space 3}0.000{col 84}{space 4}-.1539284{col 97}{space 3}-.0838026
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0052413{col 56}{space 2} .0227755{col 67}{space 1}    0.23{col 76}{space 3}0.819{col 84}{space 4}-.0405519{col 97}{space 3} .0510346
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0754632{col 56}{space 2} .0234434{col 67}{space 1}    3.22{col 76}{space 3}0.002{col 84}{space 4} .0283272{col 97}{space 3} .1225993
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} -.056713{col 56}{space 2} .0243594{col 67}{space 1}   -2.33{col 76}{space 3}0.024{col 84}{space 4}-.1056908{col 97}{space 3}-.0077352
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0122037{col 56}{space 2} .0207074{col 67}{space 1}    0.59{col 76}{space 3}0.558{col 84}{space 4}-.0294313{col 97}{space 3} .0538387
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2208  {c |}{col 44}{res}{space 2} .0244182{col 56}{space 2} .0140371{col 67}{space 1}    1.74{col 76}{space 3}0.088{col 84}{space 4}-.0038052{col 97}{space 3} .0526417
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2590112{col 56}{space 2} .0489805{col 67}{space 1}    5.29{col 76}{space 3}0.000{col 84}{space 4} .1605292{col 97}{space 3} .3574932
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:1996}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(48) = {res}    1.3156
{col 25}{txt}Prob>|t| = {res}    0.3273

95%{txt} confidence set for null hypothesis expression: {res}[−.03306, .1342]
{err}1996

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2204 {c |}{res}      2,460       49.96       49.96
{txt}       2208 {c |}{res}      2,464       50.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,924      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      4,924    .2544679    .4356059          0          1
{txt}{p 0 6 2}note: {bf:2382.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    23,428
                                                {txt}F(16, 49)         =  {res}    10.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0173
                                                {txt}Root MSE          =    {res} .45665

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0116742{col 56}{space 2} .0227518{col 67}{space 1}   -0.51{col 76}{space 3}0.610{col 84}{space 4}-.0573956{col 97}{space 3} .0340472
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .1142138{col 56}{space 2} .0496759{col 67}{space 1}    2.30{col 76}{space 3}0.026{col 84}{space 4} .0143863{col 97}{space 3} .2140413
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0421223{col 56}{space 2} .0203331{col 67}{space 1}   -2.07{col 76}{space 3}0.044{col 84}{space 4}-.0829832{col 97}{space 3}-.0012613
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0678205{col 56}{space 2} .0237995{col 67}{space 1}   -2.85{col 76}{space 3}0.006{col 84}{space 4}-.1156474{col 97}{space 3}-.0199937
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0667023{col 56}{space 2} .0230434{col 67}{space 1}   -2.89{col 76}{space 3}0.006{col 84}{space 4}-.1130097{col 97}{space 3}-.0203948
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0511051{col 56}{space 2} .0243796{col 67}{space 1}   -2.10{col 76}{space 3}0.041{col 84}{space 4}-.1000978{col 97}{space 3}-.0021124
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0298871{col 56}{space 2} .0231938{col 67}{space 1}   -1.29{col 76}{space 3}0.204{col 84}{space 4}-.0764968{col 97}{space 3} .0167225
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} -.118878{col 56}{space 2} .0575858{col 67}{space 1}   -2.06{col 76}{space 3}0.044{col 84}{space 4}-.2346011{col 97}{space 3} -.003155
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0109725{col 56}{space 2} .0070688{col 67}{space 1}   -1.55{col 76}{space 3}0.127{col 84}{space 4}-.0251778{col 97}{space 3} .0032328
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0016059{col 56}{space 2} .0003312{col 67}{space 1}    4.85{col 76}{space 3}0.000{col 84}{space 4} .0009404{col 97}{space 3} .0022714
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0218306{col 56}{space 2} .0131635{col 67}{space 1}    1.66{col 76}{space 3}0.104{col 84}{space 4}-.0046225{col 97}{space 3} .0482837
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0406847{col 56}{space 2} .0181552{col 67}{space 1}    2.24{col 76}{space 3}0.030{col 84}{space 4} .0042005{col 97}{space 3}  .077169
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0402822{col 56}{space 2} .0139048{col 67}{space 1}   -2.90{col 76}{space 3}0.006{col 84}{space 4}-.0682248{col 97}{space 3}-.0123395
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0132376{col 56}{space 2} .0121337{col 67}{space 1}    1.09{col 76}{space 3}0.281{col 84}{space 4} -.011146{col 97}{space 3} .0376211
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0090594{col 56}{space 2} .0148138{col 67}{space 1}   -0.61{col 76}{space 3}0.544{col 84}{space 4}-.0388288{col 97}{space 3} .0207099
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0133886{col 56}{space 2} .0143251{col 67}{space 1}    0.93{col 76}{space 3}0.355{col 84}{space 4}-.0153987{col 97}{space 3} .0421759
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2382  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1512645{col 56}{space 2} .0621079{col 67}{space 1}    2.44{col 76}{space 3}0.019{col 84}{space 4}  .026454{col 97}{space 3}  .276075
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2000}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}   -0.5131
{col 25}{txt}Prob>|t| = {res}    0.6857

95%{txt} confidence set for null hypothesis expression: {res}[−.1676, .09338]
{err}2000

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2382 {c |}{res}     23,428      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     23,428      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     23,428    .3051477    .4604798          0          1
{txt}{p 0 6 2}note: {bf:2555.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    23,526
                                                {txt}F(16, 49)         =  {res}    17.72
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0241
                                                {txt}Root MSE          =    {res} .43981

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0099094{col 56}{space 2} .0046959{col 67}{space 1}    2.11{col 76}{space 3}0.040{col 84}{space 4} .0004727{col 97}{space 3} .0193461
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .0714226{col 56}{space 2} .0457779{col 67}{space 1}    1.56{col 76}{space 3}0.125{col 84}{space 4}-.0205715{col 97}{space 3} .1634166
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}-.0308758{col 56}{space 2} .0217334{col 67}{space 1}   -1.42{col 76}{space 3}0.162{col 84}{space 4}-.0745506{col 97}{space 3}  .012799
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0621061{col 56}{space 2} .0242484{col 67}{space 1}   -2.56{col 76}{space 3}0.014{col 84}{space 4}-.1108351{col 97}{space 3}-.0133771
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0536619{col 56}{space 2} .0265187{col 67}{space 1}   -2.02{col 76}{space 3}0.048{col 84}{space 4}-.1069532{col 97}{space 3}-.0003706
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0430488{col 56}{space 2} .0251186{col 67}{space 1}   -1.71{col 76}{space 3}0.093{col 84}{space 4}-.0935266{col 97}{space 3}  .007429
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0324154{col 56}{space 2} .0350735{col 67}{space 1}   -0.92{col 76}{space 3}0.360{col 84}{space 4}-.1028983{col 97}{space 3} .0380674
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0732611{col 56}{space 2} .0655827{col 67}{space 1}   -1.12{col 76}{space 3}0.269{col 84}{space 4}-.2050544{col 97}{space 3} .0585322
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0491012{col 56}{space 2} .0064025{col 67}{space 1}   -7.67{col 76}{space 3}0.000{col 84}{space 4}-.0619675{col 97}{space 3} -.036235
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0024208{col 56}{space 2} .0004034{col 67}{space 1}    6.00{col 76}{space 3}0.000{col 84}{space 4} .0016101{col 97}{space 3} .0032315
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2} .0234579{col 56}{space 2} .0134668{col 67}{space 1}    1.74{col 76}{space 3}0.088{col 84}{space 4}-.0036047{col 97}{space 3} .0505205
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2} .0396031{col 56}{space 2} .0187039{col 67}{space 1}    2.12{col 76}{space 3}0.039{col 84}{space 4} .0020161{col 97}{space 3} .0771901
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} -.037877{col 56}{space 2} .0120167{col 67}{space 1}   -3.15{col 76}{space 3}0.003{col 84}{space 4}-.0620254{col 97}{space 3}-.0137287
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0105861{col 56}{space 2} .0118701{col 67}{space 1}    0.89{col 76}{space 3}0.377{col 84}{space 4}-.0132677{col 97}{space 3} .0344399
{txt}{space 34}Student  {c |}{col 44}{res}{space 2}-.0029407{col 56}{space 2} .0146413{col 67}{space 1}   -0.20{col 76}{space 3}0.842{col 84}{space 4}-.0323634{col 97}{space 3} .0264821
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0301341{col 56}{space 2} .0125062{col 67}{space 1}    2.41{col 76}{space 3}0.020{col 84}{space 4} .0050019{col 97}{space 3} .0552664
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2555  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1208433{col 56}{space 2}  .057402{col 67}{space 1}    2.11{col 76}{space 3}0.040{col 84}{space 4} .0054896{col 97}{space 3}  .236197
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2004}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}    2.1102
{col 25}{txt}Prob>|t| = {res}    0.0971

95%{txt} confidence set for null hypothesis expression: {res}[−.006525, .04478]
{err}2004

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2555 {c |}{res}     23,526      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     23,526      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     23,526    .2721245    .4450631          0          1
{txt}{p 0 6 2}note: {bf:2750.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}    17,659
                                                {txt}F(16, 49)         =  {res}     3.91
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0031
                                                {txt}Root MSE          =    {res} .46158

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0006035{col 56}{space 2} .0016623{col 67}{space 1}    0.36{col 76}{space 3}0.718{col 84}{space 4}-.0027369{col 97}{space 3} .0039439
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0006909{col 56}{space 2}  .018904{col 67}{space 1}   -0.04{col 76}{space 3}0.971{col 84}{space 4}-.0386799{col 97}{space 3} .0372981
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0099381{col 56}{space 2} .0193394{col 67}{space 1}    0.51{col 76}{space 3}0.610{col 84}{space 4}-.0289258{col 97}{space 3}  .048802
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0029263{col 56}{space 2}  .019134{col 67}{space 1}    0.15{col 76}{space 3}0.879{col 84}{space 4}-.0355248{col 97}{space 3} .0413774
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0077673{col 56}{space 2} .0238425{col 67}{space 1}    0.33{col 76}{space 3}0.746{col 84}{space 4} -.040146{col 97}{space 3} .0556805
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0228771{col 56}{space 2} .0194647{col 67}{space 1}   -1.18{col 76}{space 3}0.246{col 84}{space 4}-.0619929{col 97}{space 3} .0162387
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0259601{col 56}{space 2} .0273484{col 67}{space 1}    0.95{col 76}{space 3}0.347{col 84}{space 4}-.0289986{col 97}{space 3} .0809187
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0301797{col 56}{space 2} .0282385{col 67}{space 1}   -1.07{col 76}{space 3}0.290{col 84}{space 4}-.0869272{col 97}{space 3} .0265677
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0008884{col 56}{space 2} .0078324{col 67}{space 1}    0.11{col 76}{space 3}0.910{col 84}{space 4}-.0148515{col 97}{space 3} .0166282
{txt}{space 39}age {c |}{col 44}{res}{space 2}-.0001447{col 56}{space 2} .0003388{col 67}{space 1}   -0.43{col 76}{space 3}0.671{col 84}{space 4}-.0008255{col 97}{space 3} .0005361
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0338663{col 56}{space 2} .0112725{col 67}{space 1}   -3.00{col 76}{space 3}0.004{col 84}{space 4}-.0565192{col 97}{space 3}-.0112133
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0338721{col 56}{space 2} .0174356{col 67}{space 1}   -1.94{col 76}{space 3}0.058{col 84}{space 4}-.0689102{col 97}{space 3}  .001166
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}  .046704{col 56}{space 2} .0170365{col 67}{space 1}    2.74{col 76}{space 3}0.009{col 84}{space 4} .0124678{col 97}{space 3} .0809403
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}-.0055979{col 56}{space 2} .0138553{col 67}{space 1}   -0.40{col 76}{space 3}0.688{col 84}{space 4}-.0334412{col 97}{space 3} .0222454
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0083109{col 56}{space 2}  .019712{col 67}{space 1}    0.42{col 76}{space 3}0.675{col 84}{space 4}-.0313018{col 97}{space 3} .0479237
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2}-.0145547{col 56}{space 2} .0130315{col 67}{space 1}   -1.12{col 76}{space 3}0.269{col 84}{space 4}-.0407424{col 97}{space 3} .0116331
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2750  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .3339148{col 56}{space 2} .0368963{col 67}{space 1}    9.05{col 76}{space 3}0.000{col 84}{space 4} .2597689{col 97}{space 3} .4080607
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2008}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}    0.3631
{col 25}{txt}Prob>|t| = {res}    0.7467

95%{txt} confidence set for null hypothesis expression: {res}[−.01272, .01763]
{err}2008

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2750 {c |}{res}     17,659      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     17,659      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     17,659    .3089643    .4620795          0          1
{txt}{p 0 6 2}note: {bf:2859.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,462
                                                {txt}F(16, 47)         =  {res}     5.93
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0160
                                                {txt}Root MSE          =    {res} .40885

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0826875{col 56}{space 2}  .032145{col 67}{space 1}    2.57{col 76}{space 3}0.013{col 84}{space 4} .0180201{col 97}{space 3} .1473549
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2}-.0198115{col 56}{space 2} .0444442{col 67}{space 1}   -0.45{col 76}{space 3}0.658{col 84}{space 4}-.1092217{col 97}{space 3} .0695986
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} -.058264{col 56}{space 2} .0419441{col 67}{space 1}   -1.39{col 76}{space 3}0.171{col 84}{space 4}-.1426447{col 97}{space 3} .0261168
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0799419{col 56}{space 2} .0490819{col 67}{space 1}   -1.63{col 76}{space 3}0.110{col 84}{space 4} -.178682{col 97}{space 3} .0187982
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0799751{col 56}{space 2} .0546409{col 67}{space 1}   -1.46{col 76}{space 3}0.150{col 84}{space 4}-.1898985{col 97}{space 3} .0299483
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0595919{col 56}{space 2} .0445605{col 67}{space 1}   -1.34{col 76}{space 3}0.188{col 84}{space 4} -.149236{col 97}{space 3} .0300522
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.1014578{col 56}{space 2} .0621312{col 67}{space 1}   -1.63{col 76}{space 3}0.109{col 84}{space 4}-.2264497{col 97}{space 3}  .023534
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0775405{col 56}{space 2} .0442791{col 67}{space 1}   -1.75{col 76}{space 3}0.086{col 84}{space 4}-.1666186{col 97}{space 3} .0115375
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2} .0244547{col 56}{space 2} .0153335{col 67}{space 1}    1.59{col 76}{space 3}0.117{col 84}{space 4}-.0063923{col 97}{space 3} .0553018
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0008437{col 56}{space 2}  .000733{col 67}{space 1}    1.15{col 76}{space 3}0.256{col 84}{space 4} -.000631{col 97}{space 3} .0023183
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0381686{col 56}{space 2}  .021203{col 67}{space 1}   -1.80{col 76}{space 3}0.078{col 84}{space 4}-.0808236{col 97}{space 3} .0044864
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0528219{col 56}{space 2}  .026115{col 67}{space 1}   -2.02{col 76}{space 3}0.049{col 84}{space 4}-.1053585{col 97}{space 3}-.0002854
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0076414{col 56}{space 2}  .029968{col 67}{space 1}    0.25{col 76}{space 3}0.800{col 84}{space 4}-.0526465{col 97}{space 3} .0679292
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0126343{col 56}{space 2} .0294362{col 67}{space 1}    0.43{col 76}{space 3}0.670{col 84}{space 4}-.0465836{col 97}{space 3} .0718522
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0129717{col 56}{space 2} .0546949{col 67}{space 1}    0.24{col 76}{space 3}0.814{col 84}{space 4}-.0970602{col 97}{space 3} .1230037
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0556265{col 56}{space 2} .0348904{col 67}{space 1}    1.59{col 76}{space 3}0.118{col 84}{space 4} -.014564{col 97}{space 3}  .125817
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}2859  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .2431268{col 56}{space 2} .0599478{col 67}{space 1}    4.06{col 76}{space 3}0.000{col 84}{space 4} .1225274{col 97}{space 3} .3637263
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2011}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(47) = {res}    2.5723
{col 25}{txt}Prob>|t| = {res}    0.1431

95%{txt} confidence set for null hypothesis expression: {res}[−.01581, .1818]
{err}2011

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       2859 {c |}{res}      2,462      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,462      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,462     .214866    .4108128          0          1
{txt}{p 0 6 2}note: {bf:3050.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,466
                                                {txt}F(16, 45)         =  {res}    14.87
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0634
                                                {txt}Root MSE          =    {res}  .3259

{txt}{ralign 108:(Std. err. adjusted for {res:46} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0139621{col 56}{space 2} .0107743{col 67}{space 1}   -1.30{col 76}{space 3}0.202{col 84}{space 4}-.0356628{col 97}{space 3} .0077385
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .0953211{col 56}{space 2} .0399007{col 67}{space 1}    2.39{col 76}{space 3}0.021{col 84}{space 4}  .014957{col 97}{space 3} .1756852
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0070932{col 56}{space 2} .0421526{col 67}{space 1}    0.17{col 76}{space 3}0.867{col 84}{space 4}-.0778065{col 97}{space 3} .0919928
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0318551{col 56}{space 2}  .034367{col 67}{space 1}   -0.93{col 76}{space 3}0.359{col 84}{space 4}-.1010738{col 97}{space 3} .0373636
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2}-.0039326{col 56}{space 2} .0393669{col 67}{space 1}   -0.10{col 76}{space 3}0.921{col 84}{space 4}-.0832216{col 97}{space 3} .0753564
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2}-.0412127{col 56}{space 2} .0332647{col 67}{space 1}   -1.24{col 76}{space 3}0.222{col 84}{space 4}-.1082112{col 97}{space 3} .0257857
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2}-.0023851{col 56}{space 2} .0387892{col 67}{space 1}   -0.06{col 76}{space 3}0.951{col 84}{space 4}-.0805105{col 97}{space 3} .0757402
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0235492{col 56}{space 2} .0353044{col 67}{space 1}   -0.67{col 76}{space 3}0.508{col 84}{space 4}-.0946559{col 97}{space 3} .0475575
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0121509{col 56}{space 2} .0125022{col 67}{space 1}   -0.97{col 76}{space 3}0.336{col 84}{space 4}-.0373316{col 97}{space 3} .0130298
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0027237{col 56}{space 2} .0006898{col 67}{space 1}    3.95{col 76}{space 3}0.000{col 84}{space 4} .0013343{col 97}{space 3} .0041131
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0158423{col 56}{space 2} .0190159{col 67}{space 1}   -0.83{col 76}{space 3}0.409{col 84}{space 4}-.0541423{col 97}{space 3} .0224576
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0289213{col 56}{space 2} .0236015{col 67}{space 1}   -1.23{col 76}{space 3}0.227{col 84}{space 4} -.076457{col 97}{space 3} .0186145
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0215161{col 56}{space 2} .0154514{col 67}{space 1}   -1.39{col 76}{space 3}0.171{col 84}{space 4}-.0526368{col 97}{space 3} .0096046
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}  .052435{col 56}{space 2} .0277094{col 67}{space 1}    1.89{col 76}{space 3}0.065{col 84}{space 4}-.0033746{col 97}{space 3} .1082446
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0067069{col 56}{space 2} .0215173{col 67}{space 1}    0.31{col 76}{space 3}0.757{col 84}{space 4}-.0366312{col 97}{space 3} .0500451
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0511813{col 56}{space 2} .0368593{col 67}{space 1}    1.39{col 76}{space 3}0.172{col 84}{space 4}-.0230572{col 97}{space 3} .1254199
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3050  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}-.0654627{col 56}{space 2} .0494848{col 67}{space 1}   -1.32{col 76}{space 3}0.193{col 84}{space 4}-.1651302{col 97}{space 3} .0342048
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2015}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(45) = {res}   -1.2959
{col 25}{txt}Prob>|t| = {res}    0.4755

95%{txt} confidence set for null hypothesis expression: {res}[−.06229, .04521]
{err}2015

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3050 {c |}{res}      2,466      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,466      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,466    .1293593    .3356652          0          1
{txt}{p 0 6 2}note: {bf:3124.survey} omitted because of collinearity.{p_end}

Linear regression                               Number of obs     = {res}     2,480
                                                {txt}F(16, 47)         =  {res}    32.47
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0562
                                                {txt}Root MSE          =    {res} .37603

{txt}{ralign 108:(Std. err. adjusted for {res:48} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2} .0400779{col 56}{space 2} .0054391{col 67}{space 1}    7.37{col 76}{space 3}0.000{col 84}{space 4} .0291359{col 97}{space 3} .0510199
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .2274724{col 56}{space 2} .1806542{col 67}{space 1}    1.26{col 76}{space 3}0.214{col 84}{space 4} -.135957{col 97}{space 3} .5909018
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2} .0336667{col 56}{space 2} .0405731{col 67}{space 1}    0.83{col 76}{space 3}0.411{col 84}{space 4}-.0479558{col 97}{space 3} .1152893
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2}-.0294301{col 56}{space 2} .0386429{col 67}{space 1}   -0.76{col 76}{space 3}0.450{col 84}{space 4}-.1071696{col 97}{space 3} .0483095
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0433815{col 56}{space 2} .0386698{col 67}{space 1}    1.12{col 76}{space 3}0.268{col 84}{space 4}-.0344122{col 97}{space 3} .1211751
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0397599{col 56}{space 2} .0416101{col 67}{space 1}    0.96{col 76}{space 3}0.344{col 84}{space 4}-.0439489{col 97}{space 3} .1234686
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} -.035679{col 56}{space 2} .0479183{col 67}{space 1}   -0.74{col 76}{space 3}0.460{col 84}{space 4}-.1320782{col 97}{space 3} .0607202
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2}-.0402685{col 56}{space 2} .0470502{col 67}{space 1}   -0.86{col 76}{space 3}0.396{col 84}{space 4}-.1349212{col 97}{space 3} .0543843
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0152118{col 56}{space 2} .0147616{col 67}{space 1}   -1.03{col 76}{space 3}0.308{col 84}{space 4}-.0449083{col 97}{space 3} .0144847
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0029217{col 56}{space 2}  .001005{col 67}{space 1}    2.91{col 76}{space 3}0.006{col 84}{space 4}    .0009{col 97}{space 3} .0049435
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0092782{col 56}{space 2} .0210122{col 67}{space 1}   -0.44{col 76}{space 3}0.661{col 84}{space 4}-.0515493{col 97}{space 3} .0329928
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0156277{col 56}{space 2} .0252434{col 67}{space 1}   -0.62{col 76}{space 3}0.539{col 84}{space 4}-.0664108{col 97}{space 3} .0351554
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2}-.0459905{col 56}{space 2} .0213868{col 67}{space 1}   -2.15{col 76}{space 3}0.037{col 84}{space 4}-.0890152{col 97}{space 3}-.0029657
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2}  .049624{col 56}{space 2} .0281799{col 67}{space 1}    1.76{col 76}{space 3}0.085{col 84}{space 4}-.0070666{col 97}{space 3} .1063145
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .1146532{col 56}{space 2}  .053897{col 67}{space 1}    2.13{col 76}{space 3}0.039{col 84}{space 4} .0062264{col 97}{space 3} .2230799
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} .0572425{col 56}{space 2} .0365853{col 67}{space 1}    1.56{col 76}{space 3}0.124{col 84}{space 4}-.0163576{col 97}{space 3} .1308427
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3124  {c |}{col 44}{res}{space 2}        0{col 56}{txt}  (omitted)
{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2}-.0285467{col 56}{space 2} .0801297{col 67}{space 1}   -0.36{col 76}{space 3}0.723{col 84}{space 4}-.1897469{col 97}{space 3} .1326535
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2016}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(47) = {res}    7.3685
{col 25}{txt}Prob>|t| = {res}    0.4024

95%{txt} confidence set for null hypothesis expression: {res}[−.2094, .386]
{err}2016

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3124 {c |}{res}      2,480      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,480      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}      2,480    .1818548    .3858026          0          1

{txt}Linear regression                               Number of obs     = {res}    21,689
                                                {txt}F(18, 49)         =  {res}    26.36
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0196
                                                {txt}Root MSE          =    {res} .39317

{txt}{ralign 108:(Std. err. adjusted for {res:50} clusters in {res:prov_num})}
{hline 43}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 44}{c |}{col 56}    Robust
{col 1}                            vote_incumbent{col 44}{c |} Coefficient{col 56}  std. err.{col 68}      t{col 76}   P>|t|{col 84}     [95% con{col 97}f. interval]
{hline 43}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}top_prizes_gdp_term_2 {c |}{col 44}{res}{space 2}-.0163746{col 56}{space 2} .0176204{col 67}{space 1}   -0.93{col 76}{space 3}0.357{col 84}{space 4}-.0517843{col 97}{space 3}  .019035
{txt}{space 20}expenditure_gdp_term_2 {c |}{col 44}{res}{space 2} .0207917{col 56}{space 2} .0543628{col 67}{space 1}    0.38{col 76}{space 3}0.704{col 84}{space 4}-.0884544{col 97}{space 3} .1300378
{txt}{space 42} {c |}
{space 25}municipality_size {c |}
{space 5}Between 2,001 and 10,000 inhabitants  {c |}{col 44}{res}{space 2}  .021508{col 56}{space 2} .0159862{col 67}{space 1}    1.35{col 76}{space 3}0.185{col 84}{space 4}-.0106175{col 97}{space 3} .0536334
{txt}{space 4}Between 10,001 and 50,000 inhabitants  {c |}{col 44}{res}{space 2} .0031612{col 56}{space 2} .0176526{col 67}{space 1}    0.18{col 76}{space 3}0.859{col 84}{space 4}-.0323131{col 97}{space 3} .0386354
{txt}{space 3}Between 50,001 and 100,000 inhabitants  {c |}{col 44}{res}{space 2} .0140848{col 56}{space 2} .0189898{col 67}{space 1}    0.74{col 76}{space 3}0.462{col 84}{space 4}-.0240767{col 97}{space 3} .0522462
{txt}{space 2}Between 100,001 and 400,000 inhabitants  {c |}{col 44}{res}{space 2} .0145472{col 56}{space 2}   .01891{col 67}{space 1}    0.77{col 76}{space 3}0.445{col 84}{space 4}-.0234539{col 97}{space 3} .0525482
{txt}Between 400,001 and 1,000,000 inhabitants  {c |}{col 44}{res}{space 2} .0099158{col 56}{space 2} .0259638{col 67}{space 1}    0.38{col 76}{space 3}0.704{col 84}{space 4}-.0422603{col 97}{space 3}  .062092
{txt}{space 10}More than 1,000,001 inhabitants  {c |}{col 44}{res}{space 2} .0263812{col 56}{space 2} .0187394{col 67}{space 1}    1.41{col 76}{space 3}0.166{col 84}{space 4} -.011277{col 97}{space 3} .0640393
{txt}{space 42} {c |}
{space 36}female {c |}{col 44}{res}{space 2}-.0032871{col 56}{space 2} .0046403{col 67}{space 1}   -0.71{col 76}{space 3}0.482{col 84}{space 4}-.0126122{col 97}{space 3}  .006038
{txt}{space 39}age {c |}{col 44}{res}{space 2} .0015247{col 56}{space 2} .0002961{col 67}{space 1}    5.15{col 76}{space 3}0.000{col 84}{space 4} .0009297{col 97}{space 3} .0021196
{txt}{space 42} {c |}
{space 33}education {c |}
{space 32}Secondary  {c |}{col 44}{res}{space 2}-.0381535{col 56}{space 2} .0090312{col 67}{space 1}   -4.22{col 76}{space 3}0.000{col 84}{space 4}-.0563023{col 97}{space 3}-.0200046
{txt}{space 25}Higher Education  {c |}{col 44}{res}{space 2}-.0495785{col 56}{space 2} .0086964{col 67}{space 1}   -5.70{col 76}{space 3}0.000{col 84}{space 4}-.0670545{col 97}{space 3}-.0321024
{txt}{space 42} {c |}
{space 36}status {c |}
{space 31}Unemployed  {c |}{col 44}{res}{space 2} .0387759{col 56}{space 2} .0094044{col 67}{space 1}    4.12{col 76}{space 3}0.000{col 84}{space 4} .0198772{col 97}{space 3} .0576747
{txt}{space 34}Retired  {c |}{col 44}{res}{space 2} .0405941{col 56}{space 2} .0128332{col 67}{space 1}    3.16{col 76}{space 3}0.003{col 84}{space 4} .0148049{col 97}{space 3} .0663833
{txt}{space 34}Student  {c |}{col 44}{res}{space 2} .0133122{col 56}{space 2} .0134834{col 67}{space 1}    0.99{col 76}{space 3}0.328{col 84}{space 4}-.0137837{col 97}{space 3} .0404081
{txt}{space 30}Housekeeper  {c |}{col 44}{res}{space 2} -.008338{col 56}{space 2}  .011134{col 67}{space 1}   -0.75{col 76}{space 3}0.458{col 84}{space 4}-.0307125{col 97}{space 3} .0140366
{txt}{space 42} {c |}
{space 36}survey {c |}
{space 37}3240  {c |}{col 44}{res}{space 2} .0316737{col 56}{space 2}  .010818{col 67}{space 1}    2.93{col 76}{space 3}0.005{col 84}{space 4} .0099341{col 97}{space 3} .0534133
{txt}{space 37}3242  {c |}{col 44}{res}{space 2}-.0187934{col 56}{space 2} .0080705{col 67}{space 1}   -2.33{col 76}{space 3}0.024{col 84}{space 4}-.0350117{col 97}{space 3}-.0025751
{txt}{space 42} {c |}
{space 37}_cons {c |}{col 44}{res}{space 2} .1194339{col 56}{space 2} .0242728{col 67}{space 1}    4.92{col 76}{space 3}0.000{col 84}{space 4} .0706558{col 97}{space 3}  .168212
{txt}{hline 43}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}added macro:
           e(SurveyFE) : "{res:$\checkmark$}"

added macro:
         e(Estimation) : "{res:OLS}"

added macro:
               e(Data) : "{res:Ours}"

added macro:
            e(Outcome) : "{res:Q1}"

added macro:
         e(ProvinceFE) : "{res:$\times$}"

added macro:
               e(Year) : "{res:2019}"

Overriding estimator's cluster/robust settings with {res}cluster(prov_num)

{txt}Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by {res}prov_num{txt}, bootstrap clustering by {res}prov_num{txt}, Rademacher weights:
  {res}top_prizes_gdp_term_2

{txt}{col 28}t(49) = {res}   -0.9293
{col 25}{txt}Prob>|t| = {res}    0.6006

95%{txt} confidence set for null hypothesis expression: {res}[−.3343, .2683]
{err}2019

{txt}Survey code {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       3238 {c |}{res}      2,966       13.68       13.68
{txt}       3240 {c |}{res}      2,939       13.55       27.23
{txt}       3242 {c |}{res}     15,784       72.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}     21,689      100.00

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
vote_incum~t {c |}{res}     21,689    .1959058    .3969055          0          1
{txt}
{com}. 
. * Step 2: Save the Results to a New Dataset
. 
. * Double-check the matrix contents before conversion
. matrix list bootstrap_results
{res}
{txt}bootstrap_results[31,3]
             c1          c2          c3
 r1 {res} -.01637358  -.15246439   .06838936
{txt} r2 {res}          .           .           .
{txt} r3 {res}          .           .           .
{txt} r4 {res}          .           .           .
{txt} r5 {res}   .0262491  -.04356766   .07638973
{txt} r6 {res}          .           .           .
{txt} r7 {res}          .           .           .
{txt} r8 {res}  .03589366  -.03306457   .13417388
{txt} r9 {res}          .           .           .
{txt}r10 {res}          .           .           .
{txt}r11 {res}          .           .           .
{txt}r12 {res} -.01167418  -.16761559   .09338201
{txt}r13 {res}          .           .           .
{txt}r14 {res}          .           .           .
{txt}r15 {res}          .           .           .
{txt}r16 {res}  .00990942  -.00652482   .04477574
{txt}r17 {res}          .           .           .
{txt}r18 {res}          .           .           .
{txt}r19 {res}          .           .           .
{txt}r20 {res}  .00060351  -.01272372   .01762936
{txt}r21 {res}          .           .           .
{txt}r22 {res}          .           .           .
{txt}r23 {res}  .08268748  -.01580576   .18181378
{txt}r24 {res}          .           .           .
{txt}r25 {res}          .           .           .
{txt}r26 {res}          .           .           .
{txt}r27 {res} -.01396215  -.06228954   .04521168
{txt}r28 {res}  .04007786  -.20940855   .38599319
{txt}r29 {res}          .           .           .
{txt}r30 {res}          .           .           .
{txt}r31 {res} -.01637464  -.33431811   .26828632
{reset}
{com}. 
. * Save the results to a new dataset
. svmat double bootstrap_results, names(col)
{txt}
{com}. rename c1 top_prizes_coefficient
{res}{txt}
{com}. rename c2 top_prizes_ci_lower
{res}{txt}
{com}. rename c3 top_prizes_ci_upper
{res}{txt}
{com}. 
. * Step 3: Clean the Dataset and Calculate Standard Errors
. 
. * Drop rows where the coefficients or confidence intervals are missing
. drop if missing(top_prizes_coefficient) & missing(top_prizes_ci_lower) & missing(top_prizes_ci_upper) 
{txt}(156,453 observations deleted)

{com}. 
. * Keep only the relevant variables for the meta-analysis
. keep top_prizes_coefficient top_prizes_ci_lower top_prizes_ci_upper
{txt}
{com}. 
. * Generate a new year variable for the meta-analysis
. gen year = _n   // Adjust the starting year if necessary
{txt}
{com}. 
. replace year = 1989 if year == 1 
{txt}(1 real change made)

{com}. replace year = 1990 if year == 2
{txt}(1 real change made)

{com}. replace year =  1993 if year == 3
{txt}(1 real change made)

{com}. replace year = 1996 if year == 4
{txt}(1 real change made)

{com}. replace year =  2000 if year == 5
{txt}(1 real change made)

{com}. replace year =  2004 if year == 6
{txt}(1 real change made)

{com}. replace year = 2008 if year == 7
{txt}(1 real change made)

{com}. replace year =  2011 if year == 8
{txt}(1 real change made)

{com}. replace year = 2015 if year == 9
{txt}(1 real change made)

{com}. replace year = 2019 if year == 10
{txt}(1 real change made)

{com}. 
. * Calculate standard errors from the confidence intervals
. gen top_prizes_se = (top_prizes_ci_upper - top_prizes_ci_lower) / (2 * 1.96)
{txt}
{com}. 
. * Step 4: Meta-Analysis for top_prizes_gdp and expenditure_gdp
. 
. * Ensure that the year variable is correctly set and does not have duplicate values
. sort year
{txt}
{com}. 
. 
. *** Pooled:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}10
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}    10
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             1989{col 19}{c |}{res}{space 6}   -0.016{col 35}{space 3}   -0.127{col 47}{space 3}    0.094{col 59}{space 5} 1.19
{col 1}{txt}             1990{col 19}{c |}{res}{space 6}    0.026{col 35}{space 3}   -0.034{col 47}{space 3}    0.086{col 59}{space 5} 4.03
{col 1}{txt}             1993{col 19}{c |}{res}{space 6}    0.036{col 35}{space 3}   -0.048{col 47}{space 3}    0.120{col 59}{space 5} 2.07
{col 1}{txt}             1996{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.142{col 47}{space 3}    0.119{col 59}{space 5} 0.85
{col 1}{txt}             2000{col 19}{c |}{res}{space 6}    0.010{col 35}{space 3}   -0.016{col 47}{space 3}    0.036{col 59}{space 5}22.05
{col 1}{txt}             2004{col 19}{c |}{res}{space 6}    0.001{col 35}{space 3}   -0.015{col 47}{space 3}    0.016{col 59}{space 5}62.98
{col 1}{txt}             2008{col 19}{c |}{res}{space 6}    0.083{col 35}{space 3}   -0.016{col 47}{space 3}    0.181{col 59}{space 5} 1.49
{col 1}{txt}             2011{col 19}{c |}{res}{space 6}   -0.014{col 35}{space 3}   -0.068{col 47}{space 3}    0.040{col 59}{space 5} 5.02
{col 1}{txt}             2015{col 19}{c |}{res}{space 6}    0.040{col 35}{space 3}   -0.258{col 47}{space 3}    0.338{col 59}{space 5} 0.16
{col 1}{txt}             2019{col 19}{c |}{res}{space 6}   -0.016{col 35}{space 3}   -0.318{col 47}{space 3}    0.285{col 59}{space 5} 0.16
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.005{col 35}{space 3}   -0.007{col 47}{space 3}    0.017
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.76{txt}{col 50}Prob > |z| = {res}0.4501
{txt}Test of homogeneity: Q = chi2({res}9{txt}) = {res}4.60{txt}{col 52}Prob > Q = {res}0.8678
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_term_prizes_pooled_Q1.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_term_prizes_pooled_Q1.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. preserve 
{txt}
{com}. 
. keep if year<2010
{txt}(3 observations deleted)

{com}. 
. *** Pre:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}7
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}     7
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.03
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             1989{col 19}{c |}{res}{space 6}   -0.016{col 35}{space 3}   -0.127{col 47}{space 3}    0.094{col 59}{space 5} 1.26
{col 1}{txt}             1990{col 19}{c |}{res}{space 6}    0.026{col 35}{space 3}   -0.034{col 47}{space 3}    0.086{col 59}{space 5} 4.27
{col 1}{txt}             1993{col 19}{c |}{res}{space 6}    0.036{col 35}{space 3}   -0.048{col 47}{space 3}    0.120{col 59}{space 5} 2.20
{col 1}{txt}             1996{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.142{col 47}{space 3}    0.119{col 59}{space 5} 0.90
{col 1}{txt}             2000{col 19}{c |}{res}{space 6}    0.010{col 35}{space 3}   -0.016{col 47}{space 3}    0.036{col 59}{space 5}23.31
{col 1}{txt}             2004{col 19}{c |}{res}{space 6}    0.001{col 35}{space 3}   -0.015{col 47}{space 3}    0.016{col 59}{space 5}66.49
{col 1}{txt}             2008{col 19}{c |}{res}{space 6}    0.083{col 35}{space 3}   -0.016{col 47}{space 3}    0.181{col 59}{space 5} 1.57
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}    0.006{col 35}{space 3}   -0.007{col 47}{space 3}    0.018
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}0.89{txt}{col 50}Prob > |z| = {res}0.3750
{txt}Test of homogeneity: Q = chi2({res}6{txt}) = {res}4.04{txt}{col 52}Prob > Q = {res}0.6710
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_term_prizes_pre_Q1.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_term_prizes_pre_Q1.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. 
. restore
{txt}
{com}. 
. preserve 
{txt}
{com}. 
. keep if year>=2010
{txt}(7 observations deleted)

{com}. 
. **** Post:
. * Meta-analysis for top_prizes_gdp
. meta set top_prizes_coefficient top_prizes_se, studylabel(year)

{txt}Meta-analysis setting information

{col 2}Study information
{col 5}No. of studies: {res}3
{col 8}{txt}Study label: {res}year
{col 9}{txt}Study size: N/A

{col 8}Effect size
{col 15}Type: <generic>
{col 14}Label: Effect size
{col 11}Variable: {res}top_prizes_coefficient

{col 10}{txt}Precision
{col 10}Std. err.: {res}top_prizes_se
{col 17}{txt}CI: [{res}_meta_cil{txt}, {res}_meta_ciu{txt}]
{col 11}CI level: {res}95{txt}%

{col 3}Model and method
{col 14}Model: Random effects
{col 13}Method: REML

{com}. meta summ
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year

{txt}Meta-analysis summary{col 43}Number of studies = {res}     3
{txt}Random-effects model{col 43}Heterogeneity:
Method: REML {res}{txt}{col 55}tau2 = {res} 0.0000
{txt}{col 53}I2 (%) = {res}   0.00
{txt}{col 57}H2 = {res}   1.00

{txt}{hline 18}{c TT}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            Study{col 19}{c |}    Effect size{col 35}    [95% conf. interval]{col 59}  % weight
{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}             2011{col 19}{c |}{res}{space 6}   -0.014{col 35}{space 3}   -0.068{col 47}{space 3}    0.040{col 59}{space 5}93.95
{col 1}{txt}             2015{col 19}{c |}{res}{space 6}    0.040{col 35}{space 3}   -0.258{col 47}{space 3}    0.338{col 59}{space 5} 3.06
{col 1}{txt}             2019{col 19}{c |}{res}{space 6}   -0.016{col 35}{space 3}   -0.318{col 47}{space 3}    0.285{col 59}{space 5} 2.99
{txt}{hline 18}{c +}{hline 15}{hline 12}{hline 12}{hline 10}
{col 1}            theta{col 19}{c |}{res}{space 6}   -0.012{col 35}{space 3}   -0.064{col 47}{space 3}    0.040
{txt}{hline 18}{c BT}{hline 15}{hline 12}{hline 12}{hline 10}
Test of theta = 0: z = {res}-0.47{txt}{col 50}Prob > |z| = {res}0.6414
{txt}Test of homogeneity: Q = chi2({res}2{txt}) = {res}0.12{txt}{col 52}Prob > Q = {res}0.9402
{txt}
{com}. 
. * Store the meta-analysis results for top_prizes_gdp as scalars
. scalar top_prizes_meta_coef = r(theta)
{txt}
{com}. scalar top_prizes_meta_se = r(se)
{txt}
{com}. 
. * Generate the forest plot after storing the results
. meta forestplot, xline(0)
{res}
{txt}{col 3}Effect-size label: Effect size
{col 9}Effect size: {res}top_prizes_coefficient
{txt}{col 11}Std. err.: {res}top_prizes_se
{txt}{col 9}Study label: {res}year
{txt}
{com}. * Save the forest plot
. graph export "${c -(}figures{c )-}Survey_Appendix_forestplot_term_prizes_post_Q1.png", replace
{txt}{p 0 4 2}
file {bf}
/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Results/Figures_Survey/Survey_Appendix_forestplot_term_prizes_post_Q1.png{rm}
saved as
PNG
format
{p_end}

{com}. 
. * Combine meta-analysis results into a single model -- place holder for meta analyses 
. eststo meta_combined: estadd scalar top_prizes_gdp_term_2 = top_prizes_meta_coef

{txt}added scalar:
e(top_prizes_gdp_term_2) =  {res}-.0123792
{txt}
{com}. estadd scalar top_prizes_se = top_prizes_meta_se

{txt}added scalar:
      e(top_prizes_se) =  {res}.02658128
{txt}
{com}. 
. restore
{txt}
{com}. 
. * Step 5: Generate LaTeX Tables. Appendix Table 29
. 
. * Appendix Table 29. Part  1: years 1989 to 2011:
. esttab boots_Q1_1989 boots_Q1_1993 boots_Q1_1996 boots_Q1_2000 boots_Q1_2004 boots_Q1_2008 boots_Q1_2011 ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table29_1989_2011.tex, ///
>     keep(top_prizes_gdp_term_2) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_term_2 "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table29_1989_2011.tex"'})

{com}.         
. 
. * Appendix Table 29. Part  2: years 2015 to 2019 + meta analyses
. esttab boots_Q1_2015 boots_Q1_2016 boots_Q1_2019 meta_combined meta_combined meta_combined ///
>     using ${c -(}tables{c )-}Survey_Appendix_Table29_2015_2019_meta.tex, ///
>     keep(top_prizes_gdp_term_2) nocon r2 nostar ///
>     cells(b(fmt(3)) ci(fmt(3) par) CIstr(fmt(%-18s))) ///
>     mtitles se mtitles("Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb." "Vote Incumb.")  ///
>     coeflabels(top_prizes_gdp_term_2 "Top Lottery prizes")  ///
>         scalars("Estimation Estimation" "Data Data" "Outcome Time Outcome" "ProvinceFE Province FE" "SurveyFE Survey FE" "Year Year") replace
{res}{txt}(output written to {browse  `"Results/Tables_Survey/Survey_Appendix_Table29_2015_2019_meta.tex"'})

{com}.         
. 
. 
. /* NOTE: Information of the last column of the table (the meta-analysis) and 
> WCB confidence sets are manually added to the tables from the Stata output 
> generated here.
> */
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/carolinabernal/Library/CloudStorage/Dropbox/Spanish_Lottery/StataDos_RScripts/202406 Analysis/Ours_replication_pkg/NEW AND FINAL REPPKG/Logs/Appendix_SurveyEvidence_Tables.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}23 Oct 2025, 12:55:33
{txt}{.-}
{smcl}
{txt}{sf}{ul off}